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Regulation of HIV-1 latency by basal transcription Dahabieh, Matthew Solomon 2013

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REGULATION OF HIV-1 LATENCY BY BASAL TRANSCRIPTION by  Matthew Solomon Dahabieh  M.Sc., The University of British Columbia, 2008 B.Sc. Hon., The University of British Columbia, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUTATE STUDIES (Biochemistry and Molecular Biology)  THE UNIVERISTY OF BRITISH COLUMBIA (Vancouver)  April 2013 © Matthew Solomon Dahabieh, 2013 !  Abstract HIV/AIDS is undoubtedly one of mankind’s most pressing health concerns. Currently, there are ~34 million people infected worldwide, with ~3 million new infections and ~2 million deaths every year. Despite 30 years of research and the development of potent antiretroviral drugs, a cure for HIV-1 remains elusive. This is largely due to viral latency, a phenomenon that makes lifelong HAART therapy essential. While proviral DNA is usually transcribed, the integrated HIV-1 LTR promoter may also exist in a transcriptionally inactive latent state. Current models indicate that latency results primarily from the progressive epigenetic silencing of otherwise active infections. However, the majority of latency models utilize single reporters and selection/culturing to establish latency; therefore, they cannot differentiate between direct and progressive silencing. We hypothesize that direct LTR-silent infections are underappreciated because current models may poorly represent the entire spectrum of HIV-1 latency. In this thesis we aim to characterize a novel double-labeled Red-Green-HIV-1 vector (RGH) to comprehensively study latency. Our results show that, contrary to current dogma, the majority of RGH infections in Jurkat T cells are directly silenced. Moreover, direct silent infections are observed in several cell types and are transcriptionally competent, as known HIV-1 agonists can efficiently reactivate them. We observe that direct silencing occurs at all sites of viral integration and that cellular NFκB levels at the time of infection mediate direct LTR-silencing. Additionally, we aim to characterize the cellular transcription factor RBF-2 with respect to RGH latency and basal transcription. Our results show that RBF-2 binds two conserved sites on the HIV-1 LTR, and that this organization is necessary for mediating proper transcriptional activation. Consequently, this interaction also modulates RGH latency, as RBF-2 mutants displayed higher levels of latency relative to wild type. Collectively, our results shed new light on the previously underappreciated and immeasurable contribution of direct silent infections to HIV-1 latency. Considering these infections make up the majority of total infections in vitro, direct silencing is likely a major component of the latent reservoir in vivo. Fully understanding the entire spectrum of latency, including both direct and progressive mechanisms, will undoubtedly aid HIV-1 eradication strategies. !  ii  Preface Chapter 2 is based on a first author published paper. Dahabieh, M.S., Ooms, M., Simon, V., and I. Sadowski. (2013). A doubly fluorescent HIV-1 reporter shows that the majority of integrated HIV-1 is latent shortly after infection. Journal of Virology 87 (8): 4716-4727. Experiments were designed by myself and MO. I conducted the majority of experiments, while MO performed some supplementary work. The draft of the manuscript was written by myself, and then revised by MO, VS, and IS. Chapter 3 is based on a first author paper in preparation for submission. Dahabieh, M.S., Ooms, M., Brumme, C., Taylor, J., Harrigan, R., Simon, V., and I. Sadowski. (2013). Direct HIV-1 latency is regulated by the cellular transcription factor NFκB. Experiments were designed by myself and MO. I conducted the majority of experiments, while MO, CB, and JT performed supplementary work. The draft of the manuscript was written by myself, and then revised by MO, RH, VS, and IS. Chapter 4 is based on a first author published paper. Dahabieh, M.S., Ooms, M., Malcolm, T., Simon, V., and I. Sadowski. (2011) Identification and functional analysis of a second RBF-2 binding site within the HIV-1 promoter. Virology 418 (1): 57-66. Experiments were designed by myself and MO. I conducted the majority of experiments, with supplementary work performed by TM and MO. The draft of the manuscript was written by myself, and then revised by MO, VS, and IS.  !  iii  Table of Contents Abstract ................................................................................................................ ii Preface ................................................................................................................ iii Table of Contents ............................................................................................... iv List of Tables .................................................................................................... viii List of Figures ..................................................................................................... ix Abbreviations ...................................................................................................... xi Acknowledgments ............................................................................................. xv Dedication ......................................................................................................... xvi 1 Introduction .................................................................................................... 1 1.1 HIV-1 ......................................................................................................... 1 1.2 Viral structure and life cycle ................................................................... 1 1.2.1 Structure .............................................................................................. 1 1.2.2 Life cycle .............................................................................................. 2 1.2.2.1 Binding and entry ............................................................................ 2 1.2.2.2 Trafficking, reverse transcription and integration ............................ 2 1.2.2.3 Proviral transcription ....................................................................... 3 1.3 The HIV-1 promoter: the long terminal repeat ...................................... 5 1.4 Proviral transcriptional regulation by cellular transcription factors... 7 1.4.1 Nuclear factor kappa B (NFκB) .......................................................... 11 1.4.2 Ras-responsive binding factor 2 (RBF-2) .......................................... 12 1.5 Proviral transcriptional regulation by other factors ........................... 13 1.5.1 Chromatin organization ..................................................................... 14 1.5.2 Histone tail post-translational modifications ..................................... 14 1.5.3 DNA methylation ................................................................................ 16 1.5.4 Integration site selection .................................................................... 16 1.5.5 Non-coding RNA ............................................................................... 18 1.5.6 Tat, TAR, and pTEF-b ........................................................................ 19 1.5.7 Histone variants ................................................................................. 20 1.5.7.1 H2A variants .................................................................................. 20 1.5.7.2 H3 variants .................................................................................... 20 1.5.7.3 Histone variants and HIV-1 ........................................................... 21 1.6 HIV-1 latency .......................................................................................... 21 !  iv  1.6.1 Identifying HIV-1 latency.................................................................... 21 1.6.2 Mechanisms of HIV-1 latency ............................................................ 22 1.6.3 Consequences of HIV-1 latency ........................................................ 22 1.6.4 Modulating HIV-1 latency .................................................................. 23 1.6.5 Studying HIV-1 latency ...................................................................... 24 1.7 Research rationale, hypothesis and specific aims............................. 27 1.7.1 Rationale ............................................................................................ 27 1.7.2 Hypothesis ......................................................................................... 28 1.7.3 Specific aims ..................................................................................... 28 2 Detection of early silent HIV-1 infection using a double-labeled redgreen reporter virus .......................................................................................... 29 2.1 Introduction............................................................................................ 29 2.2 Experimental procedures ..................................................................... 30 2.2.1 Construction of a panel of red-green-HIV-1 (RGH) vectors .............. 30 2.2.2 Cell culture, virion production, and transduction .............................. 32 2.2.3 Flow cytometry and drug treatments ................................................ 32 2.3 Results .................................................................................................... 33 2.3.1 Characterization of the double-labeled HIV-1 molecular clone RGH 33 2.3.2 Red-green-HIV-1 identifies LTR-silent infections, which are established early after infection and require integration............................... 35 2.3.3 Identification of LTR-silent infections is dependent on the presence of both LTR and CMV promoters ..................................................................... 37 2.3.4 Silent LTRs in infected cells are transcriptionally competent ............ 40 2.3.5 Red-green-HIV-1 recapitulates differences in latency observed between group M subtypes .......................................................................... 43 2.4 Discussion .............................................................................................. 46 2.5 Chapter acknowledgements ................................................................ 48 3 HIV-1 latency is established early post infection and is regulated by NFκB ................................................................................................................... 49 3.1 Introduction............................................................................................ 49 3.2 Experimental procedures ..................................................................... 50 3.2.1 Vectors and constructs...................................................................... 50 3.2.2 Cell culture, virion production, and transduction .............................. 52 3.2.3 Flow cytometry and staining.............................................................. 52 3.2.4 Drug treatments ................................................................................. 53 3.2.5 Pyrosequencing of integration sites .................................................. 53 3.2.6 Chromatin immunoprecipitation (ChIP) ............................................. 53 3.3 Results .................................................................................................... 55 !  v  3.3.1 The frequency of direct LTR-silent infections is modulated by T cell signalling ....................................................................................................... 55 3.3.2 Direct LTR-silent infections are established early post infection ...... 58 3.3.3 Direct LTR-silent infections are mediated by cellular activation and NFκB 60 3.3.4 LTR-silent and LTR-active RGH infections are integrated in similar locations ....................................................................................................... 64 3.4 Discussion .............................................................................................. 71 3.5 Chapter acknowledgements ................................................................ 74 4 Identification and functional analysis of a second RBF-2 binding site within the HIV-1 promoter ................................................................................ 75 4.1 Introduction............................................................................................ 75 4.2 Experimental procedures ..................................................................... 77 4.2.1 Protein-DNA interaction assays......................................................... 77 4.2.2 Cell culture ......................................................................................... 79 4.2.3 Transient luciferase expression assays ............................................. 79 4.2.4 Viral strains ........................................................................................ 79 4.2.5 Virus production and infection assays............................................... 81 4.3 Results .................................................................................................... 82 4.3.1 RBE elements contribute to proper viral gene expression in the RGH model.82 4.3.2 Proteins in Jurkat T-cell nuclear extracts form complexes of identical mobility at both RBE3 and RBE1 sites ......................................................... 83 4.3.3 RBE1-Jurkat nuclear extract complexes contain RBF-2 and are distinct from AP4 .......................................................................................... 86 4.3.4 Recombinant USF and TFII-I bind an RBE1 containing oligonucleotide ............................................................................................. 87 4.3.5 TFII-I enhances the USF- RBE1 interaction in vitro ........................... 89 4.3.6 Interaction of RBF-2 at RBE1 can be observed by DNaseI footprinting 89 4.3.7 RBE elements mediate transcriptional activation in transient assays92 4.3.8 RBE elements are required for efficient HIV-1 virion production....... 94 4.4 Discussion .............................................................................................. 98 4.5 Chapter acknowledgements .............................................................. 101 5 Conclusion .................................................................................................. 102 5.1 Chapter summaries ............................................................................. 102 5.1.1 Chapter 2: Detection of early silent HIV-1 infection using a doublelabeled red-green reporter virus ................................................................. 102 !  vi  5.1.2 Chapter 3: HIV-1 latency is established early post infection and is regulated by NFκB ...................................................................................... 103 5.1.3 Chapter 4: Identification and functional analysis of a second RBF-2 binding site within the HIV-1 promoter ....................................................... 103 5.2 Control of HIV-1 transcription and latency ....................................... 105 5.2.1 NFκB ................................................................................................ 105 5.2.1.1 Transcription and reactivation ..................................................... 105 5.2.1.2 Silent infection ............................................................................. 105 5.2.2 AP1 .................................................................................................. 106 5.3 HIV-1 latency fluctuation .................................................................... 108 5.4 Multiple routes to HIV-1 latency......................................................... 109 5.5 Future directions ................................................................................. 111 References ....................................................................................................... 113  !  vii  List of Tables Table 1.1 Overview of transcription factors bound to the HIV-1 LTR................. 8! Table 1.2 Overview of transformed cell line based HIV-1 latency models ....... 25! Table 1.3 Overview of primary cell based HIV-1 latency models ..................... 26! Table 2.1 Oligonucleotides used in Chapter 2. ................................................ 30! Table 2.2 Plasmids used in Chapter 2. ............................................................. 31! Table 3.1 Oligonucleotides used in Chapter 3. ................................................ 50! Table 3.2 Plasmids used in Chapter 3. ............................................................. 52! Table 4.1 Double stranded oligonucleotides used in Chapter 4. ..................... 77! Table 4.2 Single stranded oligonucleotides used in Chapter 4. ....................... 78! Table 4.3 Plasmids used in Chapter 4. ............................................................. 80!  !  viii  List of Figures Figure 1.1 Schematic representation of the HIV-1 life cycle. ............................. 4! Figure 1.2 Organization of the HIV-1 genome and LTR. .................................... 6! Figure 2.1 Schematic representation of the Red-Green-HIV-1 (RGH) vector. . 34! Figure 2.2 LTR-silencing in RGH infected Jurkat cells occurs early and requires integration. .................................................................................................... 36! Figure 2.3 Identification of LTR-silent RGH infections is dependent on both the LTR and CMV promoters, and is not specific for Jurkat T cells specific...... 39! Figure 2.4 Silent LTRs in RGH infected cells are transcriptionally competent. 42! Figure 2.5 RGH recapitulates differences in latency observed between HIV-1 Group M subtypes. ....................................................................................... 44! Figure 3.1 The frequency of direct LTR-silent infections is modulated by T cell signalling. ...................................................................................................... 57! Figure 3.2 Direct LTR-silent infections are established early post infection. ... 59! Figure 3.3 RGH latency is controlled by NFκB within 4 days post infection. ... 62! Figure 3.4 Active LTR transcription in RGH infected cells is associated with activating epigenetic marks. ......................................................................... 63! Figure 3.5 Early LTR silencing occurs at all viral integration sites.................... 65! Figure 3.6 Early LTR silencing occurs regardless of genomic and epigenetic features at integration sites. ......................................................................... 68! Figure 3.7 RGH latency is not affected by heterochromatic integration sites. . 70! Figure 3.8 Model of HIV-1 latency as determined by NFκB early post infection. ...................................................................................................................... 72! Figure 4.1 RBE elements contribute to proper viral gene expression in the RGH model. ........................................................................................................... 82!  !  ix  Figure 4.2 Schematic representation of the HIV-1 LTR (LAI) and its highly conserved cis-elements. ............................................................................... 84! Figure 4.3 Proteins in Jurkat nuclear extract produce identical complexes with RBE1- and RBE3-containing oligonucleotides. ............................................ 85! Figure 4.4 Recombinant USF1/USF2 and TFII-I (RBF-2) bind RBE1, immediately 3’ of the HIV-1 TATA box. ........................................................ 88! Figure 4.5 USF binds to the TATA-proximal RBE1 element and binding is stimulated by TFII-I. ...................................................................................... 91! Figure 4.6 RBE elements mediate transcriptional activation in reporter assays. ...................................................................................................................... 93! Figure 4.7 RBE elements are required for efficient HIV-1 expression in cell culture. .......................................................................................................... 95! Figure 4.8 Infectivity defects of mutant RBE viruses are affected by input. .... 97! Figure 5.1 Summary of research aims and results in this thesis. ................... 104!  !  x  Abbreviations °C 5-aza-dC aa AIDS ANOVA AP-1 AP-4 ART ATCC BB bHLH bHLH-ZIP bp CC cDNA ChIP CMV co-IP CpG cpm CTD DBD dCTP dGTP DMEM DMSO DNA DNA DNaseI DNMT DTT EDTA eGFP EI EMSA Env FACS !  Degree Celsius 5-aza-2′deoxycytidine Amino acid Acquired immunodeficiency syndrome Analysis of variance Activating protein 1 Activating protein 4 Anti-retroviral therapy American type culture collection B-Box domain Basic helix loop helix Basic helix loop helix leucine zipper Base pair Coiled-coil domain Complementary deoxyribonucleic acid Chromatin immunoprecipitation Cytomegalovirus Co-immunoprecipitation CG dinucleotide Counts per minute C-terminal domain DNA binding domain 2’-deoxycytoosine triphosphate 2’-deoxyguanosine triphosphate Dulbecco’s modified eagle medium Dimethyl sulfoxide Deoxyribonucleic acid Deoxyribonucleic acid Deoxyribonucleic acid nuclease 1 DNA methyltransferase Dithiothreitol Ethylenediamine tetraacetic acid Enhanced green fluorescent protein Entry inhibitor Electrophoretic mobility shift assay HIV-1 envelope Fluorescence activated cell sorting xi  FBS FC g GFP GO h HAART HAT HDAC HDACi HIV-1 HMT IB II IKK INSIPID Iono IP IκB JNK kb kg L LAI LEDGF log LTR Luc m M m/v MAPK mg min miRNA mL MLL mM MNase MOI mRNA !  Fetal bovine serum Flow cytometry Gram Green fluorescent protein Gene ontology Hour Highly active anti-retroviral therapy Histone acetyltransferase complex Histone deacetylase complex Histone deacetylase complex inhibitor Human immunodeficiency virus type 1 Histone methyltransferase complex Immunoblotting Integrase inhibitor IκB kinase Integration site pipeline and database Ionomycin Immunoprecipitation Inhibitor of kappa B c-Jun N-terminal kinase Kilo base pair Kilogram Litre Prototypical subtype B HIV-1 strain (also known as LAV) Lens epithelium derived growth factor Logarithm Long terminal repeat Luciferase Metre Molarity Mass per volume Mitogen activated protein kinase Milligram Minute Micro RNA Millilitre Mixed lineage leukemia complex Millimolar Micrococcal nuclease Multiplicity of infection Messenger RNA xii  MS ncRNA NELF NFAT NFκB ng NIH ARP NLS nM NNRTI NRTI nt O/N ORF PCR pH PI PIC PKC PMA pTEF-b qPCR RBE RBF-2 RGH RING RNA RNAi RNAPII RNP rpm RPMI rRNA s SAHA SDS siRNA SP-1 ss STDEV TAR !  Mass spectrometry Non-coding RNA Negative elongation factor Nuclear factor of activated T cells Nuclear factor kappa B Nanogram National institutes of health AIDS reagent program Nuclear localization signal Nanomolar Non-nucleoside reverse transcriptase inhibitor Nucleoside analogue reverse transcriptase inhibitor Nucleotide Overnight Open reading frame Polymerase chain reaction Potential of Hydrogen Protease inhibitor Pre-integration complex Protein kinase C phorbol 12-myristate 13-acetate Positive transcription elongation factor B Quantitative PCR Ras responsive binding element Ras responsive binding factor 2 Red-Green-HIV-1 Really interesting new gene domain Ribonucleic acid RNA interference RNA polymerase II Ribonuclear protein complex Revolutions per minute Roswell park memorial institute Ribosomal RNA Second Suberoylanilide hydroxamic acid Sodium dodecylsulfate Small interfering RNA Specificity protein 1 Supershift Standard deviation HIV-1 transactivating response element xiii  Tat TBC TBE TBP TE TNFα tPIC TRIM tRNA TSA v/v VSV-g w/o YY1 µg µM  !  HIV-1 transactivator of transcription Tata binding complex Buffer consisting of Tris base, boric acid, EDTA, and water Tata binding protein Buffer consisting of Tris base, EDTA, and water Tumor necrosis factor alpha Transcription pre-initiation complex Tripartite motif Transfer RNA Trichostatin A Volume per volume Vesicular stomatitis virus Without Ying-yang 1 Microgram Micromolar  xiv  Acknowledgments I am extremely grateful and indebted to the people listed here who have helped me complete my PhD studies. Their guidance and direction have been invaluable to my success. Foremost, I would like to thank Ivan Sadowski, my research supervisor, for his insight, support, and assistance throughout my studies. I am especially thankful to Dr. Sadowski for allowing me to work largely independently. Although challenging at times, this experience has allowed me to develop skills and strengths that will be instrumental to my future career. I would also like to thank the members of my supervisory committee, LeAnn Howe and Francois Jean for their advice, criticism, and diverse perspectives on this project. I am especially grateful to Marcel Ooms and Viviana Simon (Mount Sinai School of Medicine, NY) for the time spent in their lab. Spending three months in New York was a fantastic experience in and of itself, but the techniques, methods, and research philosophy I was exposed to during my time in the Simon Lab have been pivotal to the completion of my degree and this thesis. Furthermore, I am thankful of Drs. Ooms and Simon for a productive collaboration spanning three papers and multiple conferences. I would like to thank my Sadowski Lab colleagues both past and present, including Ting-Cheng Su, Wendy Bernhard, Kris Barreto, Sheetal Raithatha, and Pedro Lourenço. I also owe special thanks to my Molecular Epigenetics Group colleagues Jacob Hodgson, Adam Chruscicki, Benjamin Martin, Nicolas Coutin, Kevin Eade, and Nancy Fang for their valuable discussions throughout my studies. I am extremely appreciative of those who funded this research and/or a portion of my studies: Canadian Institute of Health Research, Michael Smith Foundation for Health Research, and the University of British Columbia. Finally, and most importantly, I thank my parents, Elizabeth and Joseph Dahabieh for their enduring love and support during my childhood, university career, and beyond.  !  xv  Dedication To my parents, family and friends.  !  xvi  1 Introduction 1.1 HIV-1 Human Immunodeficiency Virus-1 (HIV-1) is the causative agent of Acquired Immune Deficiency Syndrome (AIDS) (Gallo et al., 1983; Barré-Sinoussi et al., 1983). In the 30 years since its discovery, HIV-1 has become endemic in many parts of the world and is one of the worlds most pressing health concerns. In 2011, approximately 34 million people were living with HIV/AIDS worldwide. Furthermore, an estimated 2.5 million new cases developed in 2011 (UN AIDS, 2012). Each year, approximately 1.7 million people die of AIDS, making HIV/AIDS the fourth leading cause of death globally and the leading cause of death in Africa (UN AIDS, 2012). Given the formidable challenges facing HIV/AIDS researchers and the seemingly unstoppable pandemic, HIV/AIDS is arguably one of the most significant health challenges we face today.  1.2 Viral structure and life cycle 1.2.1 Structure HIV-1 is a spherical, enveloped lentivirus (subset of retroviruses) that is approximately 120 nm in diameter (Gallo et al., 1983; Barré-Sinoussi et al., 1983). The HIV-1 genome, which contains nine genes, is composed of two copies of single stranded, positive sense RNA (Figure 1.2A) (Wain-Hobson et al., 1985). The genome is contained within a conically shaped capsid core structure composed of viral protein p24 monomers (Gelderblom et al., 1987). The capsid structure also contains other proteins necessary for viral replication, including reverse transcriptase, integrase, and viral protease (Gelderblom et al., 1987). Surrounding the capsid is a matrix composed of viral protein p17, and surrounding the matrix is the viral envelope, which is composed of a phospholipid bilayer membrane derived from human T cells as new virions are shed from their host (Gelderblom et al., 1987).  !  1  1.2.2 Life cycle 1.2.2.1 Binding and entry In the process of viral budding, membrane proteins of the host cell become integrated into the viral envelope as well as approximately 70 copies of the viral envelope (env) protein complex (Özel et al., 1988). The env complex consists of a cap structure composed of a trimer of the glycoprotein gp120, which is in anchored to the phospholipid envelope through a stem structure that is composed of three molecules of the integral membrane glycoprotein gp41 (Chan et al., 1997). HIV-1 fusion with target cells is mediated by the interaction of the viral envelope complex with specific receptors and co-receptors on the surface of target cells. The main receptor used by HIV-1 is the T cell co-receptor protein CD4 (Kwong and Wyatt, 1998). Infection also requires engagement of the chemokine co-receptors CCR5 or CXCR4, depending on the specific tropism of the HIV-1 strain in question (Moore, 1997; Feng et al., 1996). After fusion of the virion with the host cell, the capsid core and matrix contents are released into the cell (Chan and Kim, 1998). 1.2.2.2 Trafficking, reverse transcription and integration Trafficking of the capsid core towards the nucleus is mediated by microtubule transport mechanisms (Chan and Kim, 1998). During this process, viral RNA is converted into cDNA by the action of reverse transcriptase (Rodgers and Gamblin, 1995). Concurrent with this, the capsid core is disassembled by a mechanism involving the association of cyclophillin A (Streblow et al., 1998; Braaten et al., 1996; Gamble et al., 1996). Free viral cDNA then associates with HIV-1 integrase and other proteins to form a pre-integration complex (PIC) (De Rijck et al., 2007). This complex then enters the nucleus via the TNPO3/RANBP2 karyopherin-nuclear pore complex pathway (Ocwieja et al., 2011; Schaller et al., 2011). Once in the nucleus, the PIC is targeted to regions of active transcription by the interaction of viral integrase with the host transcriptional co-activator LEDGF/p75 (Ciuffi et al., 2005). Integration into the genome favours the outward-facing major groove of nucleosomes (AT rich sites) in the introns of actively transcribed genes (Wang et al., 2007; Brady et al., 2009). Mechanistically, integration is accomplished by insertion of the HIV-1 cDNA into  !  2  an integrase-mediated double stranded break site in the host genome and subsequent gap repair (Engelman et al., 1991). 1.2.2.3 Proviral transcription Once integrated, the viral genome (referred to as a provirus) can be transcribed by host machinery, like any other host gene. HIV-1 proviral transcription is vital for productive infection (genesis of new virions) and, consequently, transcription is tightly regulated by the interaction of host cell transcriptional machinery with the viral promoter. Additionally, repression of HIV-1 transcription results in viral latency, which has profound consequences for both pathogenesis and viral clearance. The HIV-1 life cycle is schematically depicted in Figure 1.1.  !  3  10. Budding  1. Receptor binding  CD4+ T cell 9. Assembly  CXCR4  2. Fusion  ESCRT  CD4 CCR5  Capsid core  3. Reverse transcription, trafficking, and uncoating  8. Protein trafficking Pre-integration complex  AAAA  Cytoplasm  AAAA AAAA  Nucleus  TNPO3 RanBP2/Nup358  AAAA AAAA  7. Translation  4. Nuclear import LEDGF/p75  6. Proviral transcription AAAA  AAAA AAAA  AAAA  AAAA  gag pol env  Active host gene  HIV-1 provirus  5. Integration  Figure 1.1 Schematic representation of the HIV-1 life cycle.  !  4  1.3 The HIV-1 promoter: the long terminal repeat HIV-1 transcription is driven by the Long Terminal Repeat (LTR) viral promoter (Karn and Stoltzfus, 2012). Approximately 630 bp in length, the LTR is divided into three main sections: the U3, R, and U5 regions (Figure 1.2A) (Wain-Hobson et al., 1985). While the R and U5 regions are important for viral reverse transcription, the U3 region functions as the viral promoter (Karn and Stoltzfus, 2012). The U3 portion of the HIV-1 LTR is approximately 450 bp long and is divided into a distal enhancer region and a proximal core promoter region. In total, the U3 region contains ~25-30 cis-elements that bind ~15-20 different cellular transcription factors (Figure 1.2B). In general, factors binding the core promoter are essential for HIV-1 transcription, while factors binding the distal enhancer play a modulatory role and, consequently, are somewhat dispensable.  !  5  A  HIV-1 5’ LTR U3 R U5  gag MA  CA  3’ LTR  rev NC  vif pol  tat vpu vpr  nef  5’ LTR  B nuc0  nuc1  Enhancer -455  U3 R U5  env  R  Core promoter -145  +1  U5 +96  +171  Figure 1.2 Organization of the HIV-1 genome and LTR. A: Schematic representation of the HIV-1 genome. B: Schematic representation of the long terminal repeat (LTR) promoter. Major transcription factors and their binding sites, as well as nucleosomes 0 and 1 are indicated. The transcriptional start site is denoted by an arrow and +1.  !  6  1.4 Proviral transcriptional regulation by cellular transcription factors Given the predominant T cell tropism of HIV-1, it is not surprising that the majority of transcription factors that control HIV-1 expression are part of signalling pathways downstream of the T-cell receptor. Upon engagement by antigen presentation, the T-cell receptor activates three main pathways: Ras/MAPK, PKC, and Calcinurin (Abraham and Weiss, 2004; Trushin et al., 2005). Although the effects of these pathways are pleiotropic, transcriptional regulation of target genes is predominantly mediated by the transcription factors AP-1 (c-fos/c-jun) (Duverger et al., 2012) and RBF-2 (USF1/2, TFII-I) (Dahabieh et al., 2011; Chen et al., 2005; Malcolm et al., 2008, 2007), NFκB (p65/Rel/p50) (Wu et al., 1995; Perkins et al., 1993; Williams et al., 2006; Atwood et al., 1994; Pazin et al., 1996; Desai-Yajnik and Samuels, 1993; Quivy et al., 2002) and SP1 (Perkins et al., 1993; Joel et al., 1993; Jiang et al., 2007; Desai-Yajnik and Samuels, 1993; Greger et al., 1998), and NFAT (Cron et al., 2000; Bates et al., 2008; Robichaud et al., 2002; Bosque and Planelles, 2009; Rullas et al., 2004), respectively. Consistent with their importance for regulating HIV-1, all of these factors bind the LTR within the proximal core promoter region. An overview of the transcription factors that directly bind the U3 region of the HIV-1 LTR is given in Table 1.1.  !  7  Table 1.1 Overview of transcription factors bound to the HIV-1 LTR. Transcription factor AP-1 (c-fos/c-jun)  AP4  Ets-1  Ets-2  GABP-α  CBF-1 LBP1  Cis-element location 2 sites in 5’ end of distal enhancer and 1 site in 5’ end of core promoter TATAA 3’ EBox  Overlapping with NFκB sites and distal enhancer Overlapping with NFκB sites and distal enhancer Overlapping with NFκB/Ets sites Overlapping with NFκB sites Initiator element  LSF  5’ of Initiator element  ILF GABP-β1  -283 to -195 Overlapping with NFκB/Ets sites  Fli-1  Shares Ets sites overlapping with NFκB TATAA 3’ EBox 3’ of AP-1/COUPTF in distal enhancer 3’ of AP-1/COUPTF in distal enhancer Overlapping with SP1 sites  E47 (TCF-3) cMyb  GR  c-Myc  !  Effect on LTR transcription Activating (Recruits HATs)  Reference (Karin et al., 1997; Karin, 1995)  Repressive (Inhibits TBP binding and recruits HDAC1) Activation (Recruits HATs)  (Imai and Okamoto, 2006; Ou et al., 1994) (Yang et al., 1998)  Activation (Recruits HATs)  (Yang et al., 1998)  Activation (Responds to Ras/MAPK pathway) Repressive (Recruits HDAC1) Repressive (Inhibits TBP binding)  (Flory et al., 1996)  Repressive (Recruits YY1 and HDAC1) Repressive Activation (Responds to Ras/MAPK pathway) Activating/Repressive  (Tyagi and Karn, 2007) (Kato et al., 1991) (Stojanova et al., 2004) (He and Margolis, 2002; Romerio et al., 1997) (Li et al., 1991) (Flory et al., 1996)  (Hodge et al., 1996)  Unknown Activating (Recruits HATs)  (Ou et al., 1994) (Garcia-Rodriguez and Rao, 1998)  Activating (Recruits HATs)  (Garcia-Rodriguez and Rao, 1998)  Repressive  (Stojanova et al., 2004; Brenner et al., 2005; Jiang et al., 2007)  8  Transcription factor COUP-TF C/EBPβ/IL-6 GATA-3  LEF1 NFATc1  NFκB (p50/p50) NFκB (p50/p65)  Cis-element location 2 sites in 5’ end of distal enhancer Unknown  Effect on LTR transcription Repressive  Multiple (Overlapping with other factor sites) 5’ of RBE3  Activating  Overlapping with NFκB sites and 2 sites in distal enhancer 5’ end of core promoter 5’ end of core promoter  Activating  (Billin et al., 2000)  Repressive (Recruits HDAC1) Activating (Responds to PKC/NFκB pathway – recruits CBP/p300)  (Williams et al., 2006) (Gerritsen et al., 1997; Zhong et al., 2002; Lusic et al., 2003; Thierry et al., 2004) (Marban et al., 2007, 2005; Marsili et al., 2004) (Majello et al., 1994) Majello et al., 1994) (Desai-Yajnik and Samuels, 1993)  5’ of TATAA box (3 tandem sites)  Activating/Repressive (Recruits HATs/HDACs)  SP3  Competes with SP1 sites Competes with SP1 sites Combined NFκB and SP1 sites  Repressive  T3R  Activating Activating (Responds to Thyroid hormone) Activating (Basal and Tat activated)  TBP  TATAA box  TFII-I  Initiator and RBE3 element (3’ end of enhancer)  Activating/Repressive  TMF  TATAA box  Repressive  USF1  5’ EBox and RBE3  Activating/Repressive  !  (Marban et al., 2005) (Henderson et al., 1995) (Yang and Engel, 1993)  Activating (Recruits HATs) Activating (Responds to PKC/Calcinurin pathway)  SP1  SP4  Reference  (Kinoshita et al., 1997)  (Berkhout and Jeang, 1992; Zeichner and Kim, 1991) (Montano et al., 1996; Roy et al., 1991; Dahabieh et al., 2011; Chen et al., 2005; Malcolm et al., 2007, 2008) (Garcia et al., 1992) (West et al., 2004; Chen et al., 2005;  9  Transcription factor  Cis-element location  Effect on LTR transcription  USF2  5’ EBox and RBE3  Activating/Repressive  YY1  Initiator (LSF site)  Tat (HIV-1 encoded)  TAR stem loop  Repressive (Recruits HDAC1) Activating (pTEF-b recruitment)  !  Reference Malcolm et al., 2007; Dahabieh et al., 2011; Malcolm et al., 2008) (West et al., 2004; Chen et al., 2005; Malcolm et al., 2007; Dahabieh et al., 2011; Malcolm et al., 2008) (Coull et al., 2000) (Berkhout et al., 1989)  10  1.4.1 Nuclear factor kappa B (NFκB) Originally discovered as a transcription factor regulating immunoglobulin genes in B cells (Sen and Baltimore, 1986), NFκB is a highly ubiquitous and important cellular transcription factor that is found in many organisms and almost all cell types (Hayden and Ghosh, 2012). NFκB is a downstream effector of many different signalling pathways including those responsive to stresses, cytokines, reactive oxygen species, ultraviolet light, and bacterial and viral antigens. Indeed, NFκB is a major player in regulating the immune response, not only in B cells, but also in all cells of the immune system (Hayden and Ghosh, 2012). NFκB proteins, of which there are five, fall into two classes. The first class contains two proteins NFκB1 and NFκB2 (p50 and p52, respectively) that can bind DNA but do not contain a transactivation domain (Hayden and Ghosh, 2012). The second class contains three proteins RelA (p65), RelB and c-Rel, all of which contain C-terminal transactivation domains (Hayden and Ghosh, 2012). Active NFκB is most often composed of a heterodimer of p50 and RelA/p65 subunits, and thus can function as a transcriptional activator. Of note, p50 homodimers can bind NFκB cis-elements; however, they lack activation function (Hayden and Ghosh, 2012). Although NFκB protein levels (p50 and p65) can be upregulated in response to stimuli, NFκB signalling is primarily regulated by post translational modification and protein re-localization (Hayden and Ghosh, 2012). In unstimulated cells, p65 is sequestered to the cytoplasm through the action of the NFκB inhibitor IκB. IκB contains multiple ankryin repeats that function to bind p65 and mask its nuclear localization signal (NLS), thereby keeping it in the cytoplasm (Hayden and Ghosh, 2012). In response to stimulation, IκB kinase (IKK) phosphorylates IκB at S32 and S36, thereby causing it to be degraded by the ubiquitin/proteasome system. This exposes the p65 NLS and activates NFκB signalling (Hayden and Ghosh, 2012). In T cells, NFκB is primarily responsive to the PKC and Ras/MAPK pathways downstream of the T cell receptor. For HIV-1, which is highly responsive to T cell signalling, NFκB is one of the most important and well-characterized transcription factors. The HIV-1 LTR (subtype B) contains two binding sites for NFκB in the core promoter region and, consequently, viral transcription is both highly sensitive to and absolutely dependent on NFκB activity (Karn and Stoltzfus, 2012; Colin and Van Lint, 2009). Interestingly however, while usually a !  11  transcriptional activator, NFκB can repress HIV-1 transcription via the action of p50 homodimers that recruit HDAC1 to the LTR (Williams et al., 2006). 1.4.2 Ras-responsive binding factor 2 (RBF-2) As part of the Ras-MAPK pathway, our lab has been especially interested in Ras-responsive Binding Factor 2 (RBF-2). RBF-2 is a transcription factor complex minimally composed of three subunits: a heterodimer of Upstream Stimulatory Factors (USF) 1 and 2, and the general transcription factor 2-I (TFII-I) (Chen et al., 2005). The complex was first identified from the nuclear extract of Jurkat cells, an immortalized T lymphocyte line, and was shown to be vital for LTR responsiveness to Ras/MAPK signaling (Chen et al., 2005; Bell and Sadowski, 1996). USF1 and USF2 are two highly related and ubiquitously expressed basic helixloop-helix-leucine zipper transcription factors that bind to canonical E-box elements (CANNTG), which are widely distributed across the eukaryotic genome (Corre and Galibert, 2005). Although expression of USF1 and 2 is ubiquitous amongst most cell types, the transcription factors modulate gene expression in complex ways depending on: 1) a cell-type specific ratio of USF homo- and heterodimers, 2) the interacting partners of USF at the gene of interest, and 3) the phosphorylation state of USF (Corre and Galibert, 2005). Also part of RBF-2 is another ubiquitously expressed transcription factor TFII-I (Chen et al., 2005). TFII-I was initially discovered as part of the general transcriptional machinery responsible for transcription from pyrimidine-rich initiator elements (Inr) found at many eukaryotic promoters (Roy, 2007, 2012). Subsequently, TFII-I was found in complexes with a variety of transcription factors at upstream elements. For example, independent from its initiator function, TFII-I is able to act at E-box elements through interaction with USF; thus, TFII-I is generally regarded as a unique transcription factor that facilitates communication between upstream transcriptional enhancers and the core/basal transcriptional machinery. Like USF, the function of TFII-I is multi-faceted and dependent on interaction partners as well as phosphorylation state (Roy, 2007, 2012). In the context of HIV-1, RBF-2 binds to at least one element within the HIV-1 LTR (RBE3), which flanks the viral enhancer region (Chen et al., 2005; Malcolm et al., 2007, 2008). Interestingly, RBE3 does not resemble a canonical USF site (EBox - CANNTG), but instead contains a number of spacing changes and mismatches (RBE3 – ACTGCTGA). In the absence of TFII-I, USF1/2 binds to the !  12  non-canonical RBE3 with very low affinity; however, in the presence of TFII-I, USF1/2 binding is significantly enhanced (Chen et al., 2005). Thus, this specific combination of factors (RBF-2) enables a unique specificity for one of the most stringently conserved elements on the LTR, a non-canonical E-Box (RBE3). Moreover, this interaction at RBE3 is essential for repression and activation of the integrated provirus (Malcolm et al., 2008). Indeed, in vivo mutation of RBE3 or expression of dominant negative forms of USF1/2 or TFII-I prevents induction of transcription at the LTR in response to T cell activating signals (phorbol ester – PMA treatment) (Chen et al., 2005; Malcolm et al., 2007, 2008). Furthermore, mutant RBE3 elements result in slightly higher basal level transcription from a chromosomally integrated LTR (Chen et al., 2005; Malcolm et al., 2007, 2008). Taken together, it would seem that RBF-2 plays a dual role at the proviral LTR: 1) it functions to maintain the ability to respond to T cell activation signals (Ras/MAPK, PkC, Calcinurin), and 2) it functions as a basal transcriptional repressor. The exact mechanisms of these functions must still be elucidated, however some hints already exist: 1) Previously, USF1/2 has been shown to act as a barrier to heterochromatin encroachment by binding to the insulator element at the 5’ end of the chicken β-globin locus (Huang et al., 2007). Here, USF and other proteins form a complex with both histone acetyl- and methyltransferase activity which functions to keep the β-globin locus active by maintaining a local environment of active chromatin (Huang et al., 2007b). 2) Evidence exists to support the recruitment of HDAC3 by TFII-I (Malcolm et al., 2007; Wen et al., 2003; Keedy et al., 2009). Presumably maintenance of a deacetylated state would promote transcriptional repression; however, further studies must be undertaken to elucidate the exact mechanisms involved.  1.5 Proviral transcriptional regulation by other factors Proviral HIV-1 is subject to most of the same transcriptional regulatory mechanisms as host genes. While ultimately driven by the availability and action of host sequence-specific transcription factors, downstream mechanisms of regulation include chromatin organization (nucleosome positioning), epigenetic modifications (histone tail modifications and DNA methylation), integration site effects (eu- vs. hetero-chromatin), and microRNA control.  !  13  1.5.1 Chromatin organization Eukaryotic DNA is packaged into chromatin, of which the fundamental unit is the nucleosome. This protein-DNA structure is composed of 147 bp of DNA wrapped around a histone protein octamer structure. The octamer is composed of a histone H3/H4 tetramer and two histone H2A/H2B dimers (Luger et al., 1997). Various levels of chromatin compaction (e.g. from 10 nm beads-on-string to metaphase chromosomes) regulate gene expression by imposing physical blocks on the access of transcriptional machinery to the target DNA sequence (Lusser, 2002; Cremer and Cremer, 2001; Fraser and Bickmore, 2007). In the context of HIV-1, two highly positioned nucleosomes are known to exist within the U3 LTR (Nuc0 and Nuc1) (Colin and Van Lint, 2009). Nuc0 is positioned within the distal enhancer, while nuc1 is within the proximal promoter and directly overtop of the transcriptional start site (Rafati et al., 2011; Verdin et al., 1993). In the repressed state, this nucleosome organization is highly restrictive to HIV-1 transcription by blocking formation of the transcriptional preinitiation complex (tPIC). Numerous studies have shown that upon T cell activation, nuc1 is actively remodeled by ATP-dependent histone chaperones, specifically the SWI/SNF family member PBAF and the MI2/CHD family member NURD (Van Duyne et al., 2011; Rafati et al., 2011; Van Lint et al., 1996a; Verdin et al., 1993; Angelov et al., 2000); failure to remodel nuc1 results in constitutive transcriptional repression. Interestingly, the positioning of these two nucleosomes seems to be an active and highly conserved process, with each provirus at unique sites of integration having the same positioning. Indeed, the BAF remodeler complex is necessary to actively place nuc1 overtop of the transcriptional start site (Rafati et al., 2011). In its absence, nuc1 favors an alternative position in the core promoter overtop of the NFκB/NFAT binding sites, which is correlated with increased basal transcription (Rafati et al., 2011). Initial evidence suggests that BAF recruitment to the early provirus may be mediated by the transcription factors YY1/LSF1 (Rafati et al., 2011). 1.5.2 Histone tail post-translational modifications While able to impose physical blocks to transcription, nucleosomes can also regulate transcription by means of post-translational modifications. Histone Nterminal tails are highly basic and, as such, contain a large number of lysine residues that can be modified by acetylation, methylation, ubiquitylation, etc. (Bernstein et al., 2007; Berger, 2002). !  14  Lysine acetylation, which is catalyzed by histone acetyltransferase complexes (HATs), is associated with active transcription and is thought to function, at least in part, by neutralizing the positive charge of lysine residues (Bussiek et al., 2006; Simon et al., 2011; Shimko et al., 2011). This neutralization weakens the electrostatic interaction with the negatively charged phosphate backbone of DNA and reduces chromatin compaction, thereby promoting transcription. Additionally, lysine acetylation may be recognized and bound by bromodomains found within other chromatin modifying proteins (Zeng and Zhou, 2002). Lysine methylation is catalyzed by histone methyltransferases (HMTs) and is different from acetylation in that it does not neutralize the positive charge of the lysine residue (Pomerantz and Zhang, 2001; Bannister et al., 2002; Kouzarides, 2002). Methylation can exist in three forms: mono, di, or tri, and each modification state can have differential effects on transcription depending on the residue that is modified. Well-characterized methylations include H3K4me3 (activating), H3K36me3 (activating), H3K9me3 (repressive), and H3K27me3 (repressive) (Kouzarides, 2007). Methylation marks can also be recognized by chromodomains, and serve as a recruitment mark for subsequent chromatin modifiers (Jones et al., 2000). Not surprisingly, a number of histone modifications have been found at the HIV1 LTR. Active transcription is associated with hyper-acetylation of both nuc0 and nuc1 by HATs such as CBP/p300 and P/CAF/GCN5 (Colin and Van Lint, 2009; Van Lint et al., 1996a; Kiernan et al., 1999; Lusic et al., 2003; Stevens et al., 2006). Indeed, nuc1 has been shown to be specifically hyper-acetylated on numerous residues including H3K9, H3K14, H4K8, and H4K16 prior to its remodeling by PBAF/NURD (Lusic et al., 2003). Active LTR transcription is also associated with H3K4me3 catalyzed by the COMPASS/MLL complex (Shilatifard, 2012). Repression of LTR transcription has been associated with the recruitment of histone deacetylase complexes (HDACs) to the LTR and subsequent deacetylation of nucleosomes. HDACs 1, 3, and 7 have been shown to regulate the HIV-1 LTR (Keedy et al., 2009), and are recruited by various transcription factors. For example, NFκB p50 homodimers (Williams et al., 2006), SP1 (Jiang et al., 2007), AP4 (Imai and Okamoto, 2006), and YY1 (Coull et al., 2000) all recruit HDAC1, while TFII-I recruits HDAC3 (Tussie-Luna et al., 2002). SP1 can also recruit HDAC2 via interaction with the co-repressor CTIP-2 (Marban et al., 2005, 2007). !  15  Additionally, repression of LTR transcription has been associated with the HP1 and polycomb silencing pathways. In the former case, recruitment of the HMT Suv39h1 by SP1/CTIP-2 causes H3K9me3 and HP1 recruitment (Marban et al., 2005, 2007; Bernhard et al., 2011; Pearson et al., 2008). In the latter case, the polycomb complex PRC2 has been shown to repress HIV-1 by deposition of H3K27me3 (Friedman et al., 2011). This mark causes subsequent recruitment of PRC1 and silencing effectors such as DNMTs (Schwartz and Pirrotta, 2007). However, it is currently unclear how PRC2 is initially recruited to the HIV-1 LTR. 1.5.3 DNA methylation In many mammalian promoters, DNA methylation serves a final “lock” of a repressed gene state. That is, inducible genes are rarely repressed by DNA methylation, however permanently silenced genes are (e.g. pluripotency genes in terminally differentiated cells, as well as endogenous retro-elements) (Jones and Takai, 2001). De novo DNA methylation is deposited by DNMT3A, 3B, and 3L, while methylation is maintained by DNMT1 (Okano et al., 1999). Active genes often contain CpG islands that are kept demethylated until permanent silencing (Bird, 2002, 1986; Razin and Riggs, 1980). DNA methylation may repress transcription by preventing transcriptional activators from binding their respective cis-elements (Bednarik et al., 1987, 1991) or it may cause the recruitment of methyl-CpG-binding domain containing proteins such as MBD2, which may then recruit HDACs and other repressors (Hendrich and Bird, 1998). Although DNA methylation has been shown to repress HIV-1 in certain studies (Blazkova et al., 2009; Gutekunst et al., 1993), its role in vivo is relatively unclear. Effects of DNA methylation seem to be dependent on the model system used and the study design (Pion et al., 2003; Blazkova et al., 2012; Fernandez and Zeichner, 2010). Despite this, whenever DNA methylation was shown to be involved in HIV-1 proviral silencing, it seems to be a late silencing mark, consistent with its role at host promoters. 1.5.4 Integration site selection Differential regulation of host cell genes leads to distinct chromatin microenvironments across the genome. One of the best-characterized distinctions is the contrast between euchromatin and heterochromatin. Euchromatin is generally rich in actively transcribed genes and corresponding activating epigenetic modifications. In contrast, heterochromatin, which is gene !  16  poor and not transcribed, is heavily marked with repressive histone modifications and is most often found condensed into higher order chromatin structures (Carmo-Fonseca, 2002; Grunstein et al., 1995; Grewal and Moazed, 2003). The existence of these microdomains has distinct consequences for the expression of viruses and transgenes inserted into the genome. For example, in a Avian sarcoma leukosis model, integrations away from the promoters of genes (outside the activating H3K4me3 enriched region) resulted in proviruses that are more efficiently silenced, relative to proviruses inside the promoters of genes (Senigl et al., 2012). HIV-1 integrates into the genome in a semi-random fashion. PICs are targeted to regions of active transcription by the association of viral integrase with the transcriptional co-activator LEDGF/p75 (Ciuffi et al., 2005) and, to a much lesser extent, another transcriptional co-regulator HRP-2 (Schrijvers et al., 2012). This results in distinct integration site selection in the introns of actively transcribed, gene dense regions that are enriched for canonical activating histone marks such as H3K4me3 and lysine acetylation, as well as depleted for repressive histone marks such as H3K9me3 and H3K27me3 (Wang et al., 2007; Brady et al., 2009). This site selection profile generally promotes the transcription of integrated proviruses. However, chance integrations can occur outside of these regions. Indeed, specific latent clones have been identified in which the virus is integrated into centromeric alphoid repeats, which are regions of heterochromatin that are highly repressive to transcription (Jordan et al., 2003). Additionally, HIV-1 transcription can be controlled by transcriptional interference from the host gene into which the virus integrates. This process, which is most efficient when the virus is in a parallel orientation relative to the host gene, occurs when transcriptional machinery of the actively transcribed host gene disrupts transcription factor and tPIC binding at the HIV-1 LTR (Han et al., 2008; Lenasi et al., 2008). Paradoxically, HIV-1 chooses actively transcribed genes, which promotes its transcription, yet also repress it by transcription interference.  !  17  1.5.5 Non-coding RNA The role of non-coding RNA (ncRNA) in controlling gene expression is only just starting to be understood. ncRNAs may be generated by multiple mechanisms including anti-sense transcription, and transcription of repetitive regions/transposons, amongst others. These processes give rise to both small interfering RNA (siRNA) and micro RNA (miRNA) (Moazed, 2009; Zaratiegui et al., 2007). ncRNAs may go on to be processed by Dicer/Drosha ribonucleases, and packaged into numerous downstream effector complexes composed of various Argonaut proteins and other target specific proteins (Moazed, 2009; Zaratiegui et al., 2007). By utilizing the sequence specificity of the ncRNA component, these complexes control gene expression through multiple mechanisms including: mRNA capping inhibition, ribosomal inhibition, elongation inhibition, premature translational termination, co-translational protein degradation, P-body sequestration, mRNA decay, mRNA cleavage, and transcriptional inhibition by chromatin modification (Sun et al., 2010; Morozova et al., 2012). In the context of HIV-1, miRNAs of both host and viral origin can modulate gene expression. A specific subset of miRNA upregulated in resting CD4+ T cells can downregulate the expression of HIV-1 accessory proteins including Tat and Rev, both of which are vital to productive infection of T cells (Huang et al., 2007a). Moreover, the miRNA Hsa-miR29a has been shown to downregulate the HIV-1 pathogenicity factor Nef (Ahluwalia et al., 2008), while other miRNAs have been associated with HIV-1 inhibition by inducing general mRNA translational inhibition (Chable-Bessia et al., 2009). While cellular miRNAs have been associated with viral restriction, a number of viral proteins and miRNAs have been associated with modulating HIV-1 expression. HIV-1 infection has been shown to downregulate the cellular miRNAs miR-17-5p and 20a, which are negative regulators of the HAT complex P/CAF/GCN5 (Triboulet et al., 2007). Additionally, HIV-1 TAR (Ouellet et al., 2008; Klase et al., 2007) and Nef (Omoto et al., 2004; Omoto and Fujii, 2005) RNAs can be processed into miRNA that have been shown to promote transcriptional silencing by downregulating LTR transcription directly, or by recruiting HDAC1 to the LTR promoter. Finally, Tat (Bennasser et al., 2006, 2005) and TAR (Christensen et al., 2007) can affect the RNAi machinery directly, by modulating Dicer levels.  !  18  1.5.6 Tat, TAR, and pTEF-b In addition to regulation by cellular factors, HIV-1 transcription is also regulated by the virally encoded trans-activator, Tat. Tat is a small (11 kDa) multifunctional protein that can be secreted from infected cells and absorbed by uninfected cells to induce apoptosis (Campbell et al., 2004; Debaisieux et al., 2012). In addition, it promotes CCR5 (macrophage tropic) co-receptor usage early in infection by antagonizing the CXCR4 (T cell tropic) co-receptor (Xiao et al., 2000); this may be important for pathogenesis and the switch to CXCR4 usage late in infection. Its main function however, is to facilitate high level HIV-1 transcription by initiating a positive feedback loop (Berkhout et al., 1989). Like the other HIV-1 accessory proteins Rev and Nef, Tat is an early HIV-1 gene product. It is produced from short, multiply spliced transcripts early after infection (Karn and Stoltzfus, 2012). Once translated, Tat binds to the TAR stem loop in the 5’ end of nascent HIV-1 transcripts (Berkhout et al., 1989). This process serves to enhance HIV-1 transcription up to 100 fold by recruiting the cellular positive elongation factor pTEF-b (CDK9/CyclinT1) (Tahirov et al., 2010). pTEF-b is tightly regulated in cells and is usually inactivated by sequestration in the 7SK ribonuclear protein complex composed of 7SK RNA, HEXIM1/2 and Cyclin T1 (Nguyen et al., 2001; Yang et al., 2001; Yik et al., 2003; Michels et al., 2004). Tat liberates pTEF-b from the 7SK complex and recruits it to the HIV-1 LTR where it strongly promotes transcription by phosphorylation of CBP/p300 and mediator (Barboric et al., 2007; Krueger et al., 2010; Sedore et al., 2007). Tat-pTEF-b also functions to phosphorylate Negative Elongation Factor (NELF) (Fujinaga et al., 2004) thereby relieving transcriptional repression, as well as phosphorylate the C-terminal domain of RNA polymerase II on Ser2 (Isel and Karn, 1999), which promotes transcriptional elongation. Both of these actions further potentiate the positive effects of Tat-pTEF-b on transcription, which not only increases transcriptional output, but allows for the generation of minimally and unspliced transcripts encoding both the late HIV-1 gene products (gag, pol, and env), and genomic RNA, respectively (Karn and Stoltzfus, 2012). Since Tat stimulates its own transcription, the Tat-TAR-pTEF-b axis forms a positive feedback loop (Weinberger et al., 2005). However, transcription from the HIV-1 LTR can be divided into two phases: Tat-independent, and Tat-dependent (Karn and Stoltzfus, 2012). The Tat-independent phase occurs early after infection and is regulated solely by cellular factors. Transcriptional output during this period is generally linear and short transcripts predominate (Karn and !  19  Stoltzfus, 2012). The Tat-dependent phase occurs later on after Tat has reached a threshold level in the cell. In this phase, transcriptional output is exponentially higher (approaching plateau) and the full range of HIV-1 transcripts is produced (Karn and Stoltzfus, 2012). Interestingly, the threshold nature of the Tat positive feedback loop combined with other regulatory mechanisms yields generally binary HIV-1 gene expression (on or off). Moreover, relative to other promoters, this architecture produces a fairly narrow range of expression levels when HIV-1 transcription is active (Weinberger and Shenk, 2007; Weinberger et al., 2008; Singh et al., 2010). 1.5.7 Histone variants While the canonical nucleosome is composed of two copies each of H2A, H2B, H3, and H4 (Luger et al., 1997), a number of histone variant proteins have been described to be important to the epigenetic regulation of gene expression. More specifically, the incorporation of different variants to both H2A and H3 has been shown to have distinct effects on transcription (Ausió, 2006). Variants for H2A include H2A.Z, H2A.Bbd, H2A.X and macroH2A. H3 variants include H3.3, CENP-A, H3t, and H3.5. 1.5.7.1 H2A variants H2A variants H2A.Z and H2A.Bbd are both involved in transcriptional activation, albeit at different locations in the genome. H2A.Z is classically deposited in the nucleosomes flanking active gene promoters (-1 and +1 nucleosomes) (Zhang et al., 2005), while H2A.Bbd is specifically incorporated into the active X chromosome of female mammals (Chadwick and Willard, 2001). In contrast, macroH2A is involved in gene silencing, and is enriched at the inactive X chromosome, inactive alleles of imprinted genes (Costanzi and Pehrson, 1998), and at polycomb-silenced regions of the genome (Buschbeck et al., 2009). H2A.X is an H2A variant involved in DNA repair. It is specifically recruited to, and phosphorylated at, sites of DNA damage, where it functions to recruit repair machinery (Rogakou et al., 1998). 1.5.7.2 H3 variants Like H2A variants, H3 variants are multi-functional. The most well described H3 variant is H3.3, which is associated with transcriptional activation, and is !  20  enriched at gene promoters and sites of H3K4me3 (Mito et al., 2005). CENP-A is a centromere specific H3 variant required for kinetochore function and chromosome segregation (Sullivan et al., 1994), while H3t and H3.5 are testis specific variants with unclear function (Hake and Allis, 2006; Schenk et al., 2011). 1.5.7.3 Histone variants and HIV-1 Although much is known about other epigenetic marks and HIV-1 transcriptional regulation (Chapter 1.5), understanding of the potential role for histone variants in HIV-1 regulation is curiously absent. To our knowledge, incorporation of histone variants into HIV-1 nucleosomes has not been studied. However, it seems entirely plausible that active HIV-1 expression may be correlated with variants associated with transcriptional activation, such as H2A.Z and H3.3.  1.6 HIV-1 latency 1.6.1 Identifying HIV-1 latency In the first five years after the emergence of HIV-1/AIDS, medicine was powerless to fight the disease. The development of AZT (nucleoside reverse transcriptase inhibitor) in 1986 paved the way for the development of antiretroviral therapy (Broder, 2010). However, it was quickly appreciated that mono-therapy was not sufficient to completely halt viral replication and that drug resistant strains were beginning to emerge (Richman, 1990). The real breakthrough in HIV-1/AIDS therapeutics came in 1996 with the development of HAART (combination therapy of two nucleoside reverse transcriptase inhibitors and one of a non-nucleoside reverse transcriptase inhibitor, protease inhibitor, integrase inhibitor, or entry inhibitor) (Ho, 1995). HAART was shown to completely block viral replication and, as such, was much less likely to generate drug resistant strains. More importantly, HAART was shown to stably reduce plasma viremia to undetectable levels and, in turn, boost CD4+ T cell counts (Porter, 2000; Li et al., 1998; Kaufmann et al., 1998). The success of HAART therapy produced considerable optimism that prolonged suppression of viral replication would result in viral eradication and a cure. However, it was quickly determined that a stable viral latent reservoir exists, as cessation of therapy results in complete rebound of viral load, even after years !  21  of highly effective HAART (Chun et al., 1997b; Isada and Calabrese, 1999; Finzi et al., 1997; Siliciano, 1998). 1.6.2 Mechanisms of HIV-1 latency Upon infection, HIV-1 integrates into the host genome and subsequent transcription by the cellular machinery leads to an accumulation of genomic and spliced viral RNAs. However, the HIV-1 LTR can also be transcriptionally inactive and, as a result, no viral proteins are produced. Transcriptionally inactive, latent HIV-1 can form as a result of multiple and progressive epigenetic mechanisms that silence otherwise productive infections (Siliciano and Greene, 2011; Colin and Van Lint, 2009 and Chapter 1.5). These mechanisms include, but are not limited to: a) host cell activation state and transcription factor pools, b) HIV-1 LTR chromatin structure and modifications (e.g., nucleosome positioning, histone tail modification, DNA methylation), c) proviral genomic location (e.g., host chromatin environment, provirus orientation relative to host genes), d) threshold levels of the viral transactivator Tat, and e) upstream control of HIV-1 activation by a currently unknown protein kinase (Wolschendorf et al., 2012). Cumulatively, these molecular mechanisms have lead to the prevailing view that HIV-1 latency is largely the product of progressive epigenetic silencing of active infections. HIV-1 transcriptional regulatory mechanisms all play some role in suppressing viral replication and promoting latency. However, the single greatest challenge in understanding HIV-1 latency is determining the relative contributions of each mechanism to yield the final latent phenotype. Our collective understanding of the cumulative effects of HIV-1 transcriptional regulation, and their impact on latency, is extremely limited. Despite this, the multiple levels of transcriptional control suggest that the various mechanisms of HIV-1 latency may function redundantly, or at least in parallel, with interplay between mechanisms. 1.6.3 Consequences of HIV-1 latency Post-integration HIV-1 latency is one of the main obstacles to viral eradication, since latently infected cells are not detected by the immune system and are not affected by HAART (Finzi et al., 1999; Siliciano et al., 2003; Richman et al., 2009). The latent reservoir is formed within days of initial infection (Chun et al., 1997a; Wong et al., 1997; Carter et al., 2010), and is primarily composed of long lived resting memory CD4+ T cells, although other cell types and/or anatomical !  22  sanctuaries may exist (Chun et al., 1998). Latency makes lifelong HAART a requirement for HIV-1 infected individuals (Choudhary, 2011), as cessation of therapy results in complete viral rebound within weeks (Ho and Zhang, 2000). Due to the extremely slow turnover of latently infected memory CD4+ T cells, mathematical models indicate that on current HAART, complete clearance of HIV-1 would take approximately 70 years (Siliciano et al., 2003; Zhou et al., 2005; Siliciano and Greene, 2011). Moreover, HAART therapy is generally not 100% effective in viral suppression, is poorly tolerated amongst some patients, is expensive, and is beginning to be matched by the emergence of treatment resistant HIV-1 strains (Stevenson, 2003). For long-term management of the HIV-1 epidemic it is imperative that, in addition to the development of effective vaccines, strategies be developed to target both the latent and replicating virus. 1.6.4 Modulating HIV-1 latency Eradication of HIV-1 will require novel pharmacotherapies directed at modulating the latent reservoir. One of the most commonly proposed strategies, termed “Shock and Kill”, would utilize small molecule compounds that are able to potently and specifically induce latent HIV-1 proviral expression in a broad range of epigenetic environments and cellular states. This strategy was put forth after various studies of HAART intensification regimens indicated that HAART therapy alone, even intensified, cannot purge the latent reservoir (Gandhi et al., 2010; Dinoso et al., 2009). To date, “shock and kill” efforts to purge the latent reservoir have focused on the usage of histone deacetylase inhibitors (HDACi) to induce LTR transcription. While able to induce HIV-1 expression ex vivo, early efforts with these drugs showed little impact on the latent reservoir in vivo (Siliciano et al., 2007; Archin et al., 2008; Sagot-Lerolle et al., 2008); however, a recent study demonstrated the induction of plasma viremia in patients treated with the HDACi suberoylanilide hydroxamic acid (SAHA), thereby providing the first in vivo proof of principle (Archin et al., 2012). Importantly however, recent evidence suggests that latently infected cells that are activated by HDACi may not be effectively killed by viral cytopathic effects alone (Shan et al., 2012). Instead, effective killing of latently infected cells may require co-stimulation of HIV-1 specific CD8+ cytotoxic lymphocytes (CTL) (Shan et al., 2012). Therefore, any efficacious therapeutic “shock and kill” regimen may have to target both latently infected CD4+ T cells and HIV-1 specific CTLs.  !  23  1.6.5 Studying HIV-1 latency Given that latently infected cells in HIV-1+ patients are extremely rare (~1 per 106 CD4+ T cells), as well as undistinguishable from uninfected cells (Siliciano et al., 2003), studies of latency are absolutely reliant on in vitro cell models. Early models focused on the use of transformed cell lines (e.g. Jurkat, CEM, or SupT1); however, recent efforts have focused on primary cell models of latency, which are thought to better recapitulate quiescent memory CD4+ T cells latently infected in vivo. Moreover, recent primary cell models have been specialized to certain CD4+ T cell compartments by polarizing cells to central or effector memory lineages prior to infection (Bosque and Planelles, 2009). Typically, latency models utilize a single reporter gene linked to the HIV-1 LTR to track infection status and viral transcription. Additionally, infection at low multiplicities of infection (MOI) dictates that infected cells generally make up only a small fraction of the total cell population. Thus, models may utilize selection methods (typically FACS, and either positive or negative) to enrich for infected cells. For example, in a positive selection study (Kutsch et al., 2002), researchers infected Jurkat cells with an eGFP expressing virus, selected for eGFP+ cells, and then cultured cells until eGFP expression ceased. Cells that could be chemically induced to produce eGFP again were considered latently infected. In a negative selection study (Jordan et al., 2003), researchers again infected Jurkat cells with an eGFP expressing virus, but performed a first selection against eGFP. Sorted cells were then chemically induced to express eGFP, at which point positive cells were selected. Again, cells were cultured until eGFP expression ceased and then were considered latently infected. An overview of current in vitro transformed cell line based latency models is given in Table 1.2. An overview of primary cell based latency models is given in Table 1.3.  !  24  Table 1.2 Overview of transformed cell line based HIV-1 latency models Model Name  !  Markers  Selections  ACH-2  None  None  Predominant Latency Mechanism Mutant TAR  U1  None  None  Mutant Tat  J89  LTR-eGFP  Unknown  JLat  LTR-eGFP  rT-Ta  None  Positive FACS Negative FACS None  Heterochromatic integrations Doxycycline control  Reference  (Folks et al., 1989; Emiliani et al., 1996) (Folks et al., 1987; Emiliani et al., 1998) (Kutsch et al., 2002, 2003) (Jordan et al., 2003) (Jeeninga et al., 2008)  25  Table 1.3 Overview of primary cell based HIV-1 latency models Model  Markers  Selections  CD4+ Subset  MOI  Vector  % Prod. cells  % Lat. cells  Comp. time  Ref.  Activated CD4+ T cells Sahu None  None  TCM  1-10  Rep. comp  ~80%  >5%  2 mths  Tyagi  LTR-d2EGFP  None  TCM  N/A  Δgag/Δtat  ~85%  20%  2 mths  Marini  None  None  TCM  0.002  Rep. comp  5-10%  2%  2 mths  Bosque  None  None  TCM/TEM  50  Δenv  3%NP/14.5% Th1/3%Th2  45%NP/11% Th1/40%Th2  1 mth  Yang  drEGFP  Negative  TEM  <0.01  5-10%  1-3%  ~4 mths  eGFP or Luc. or mCherry+Luc Resting CD4+ T cells Swiggard None  None  TCM/TEM  >10  Δgag/Δvif/Δvpr /Δvpu/Δenv Rep. comp (Luc. version is Δ nef)  5-10%  1-3%  Several days  (Sahu et al., 2006) (Tyagi et al., 2010) (Marini et al., 2008) (Bosque and Planelles, 2009) (Yang et al., 2009) (Lassen et al., 2012)  None  22-150  Rep. comp  0.3%  4.5%  Saleh  None  Naïve/ TCM/TEM Naïve/ TCM/TEM  1  Rep. comp  N/A  N/A  Several days Several days  (Swiggard et al., 2005) (Saleh et al., 2007)  Lassen  eGFP  Adapted from (Hakre et al., 2012)  !  26  1.7 Research rationale, hypothesis and specific aims 1.7.1 Rationale HIV-1 latency is undoubtedly one of the most significant barriers to viral eradication. Clinical trials are currently underway to examine the efficacy of reservoir purging compounds like HDACi’s. However, it is undeniable that a thoroughly comprehensive understanding of HIV-1 latency would aid the search for novel therapeutics. This understanding means considering all aspects of latency, including establishment, maintenance, and subsequent expression. Moreover, latency must be examined in an unbiased manor that studies all HIV1 infected cell fates equally. As a result of their design, current HIV-1 latency models cannot simultaneously study and discriminate between initial (direct) and secondary (progressive) HIV-1 silencing/latency. This has led to the neglect of LTR-silent infections in HIV-1 latency formation. In addition, current latency models utilize selection methods and subsequent cell expansion to enrich for infected cells, thereby excluding specific infected populations, and forgoing the ability to study latency establishment. Thus, current models likely do not reflect the entire spectrum of HIV-1 latency. Ideally, a HIV-1 latency model would fulfill the following criteria, with items 1, 2, and 3 being the most important: 1. 2. 3. 4. 5. 6. 7. 8. 9.  Robust, high-throughput LTR readout Contains LTR-independent infection marker Established without ‘selection’ bias Derived from replication-competent HIV-1 Expresses full complement of HIV-1 genes Follows the true HIV-1 lifecycle Infection performed at low MOI Compatible with primary cells Capable of generating high quantity of latent cells for biochemical analysis 10. Compatible with high-throughput drug screening  !  27  While HIV-1 latency is canonically regarded as being the result of progressive silencing of active infections, it stands to reason that direct silent-infections may be a significant source of latency. Given the strong positive feedback of the TatTAR-pTEF-b axis, direct LTR-silent infections may be formed in situations where Tat-independent transcription cannot produce threshold levels of Tat. Consequently, cellular conditions, which yield low basal HIV-1 transcription, may be responsible for the formation of LTR-silent infections. 1.7.2 Hypothesis A ‘double-labeled’ HIV-1 construct should allow for LTR-independent assessment of infection and, therefore, be useful for studying all aspects of latency (establishment, maintenance, and activation) without the biases of ‘selection’ based models. Furthermore, the occurrence of direct LTR-silent infections should be a function of Tat-independent LTR transcriptional output, as determined by the action and availability of basal transcription factors. 1.7.3 Specific aims 1. Construct and characterize a novel ‘double-labeled’ HIV-1 vector suitable for studying HIV-1 latency. 2. Examine the occurrence of direct LTR-silent infections. 3. Elucidate mechanisms governing direct LTR-silent infections. 4. Characterize the role of RBF-2 in mediating basal HIV-1 transcription and latency.  !  28  2 Detection of early silent HIV-1 infection using a double-labeled red-green reporter virus 2.1 Introduction HIV-1 latency is regarded as one of the most significant barriers to viral eradication and radical new strategies are being developed to purge the latent reservoir from infected individuals. However, such strategies are based on in vitro HIV-1 latency models that are inherently limited because they cannot examine all populations of latent cells and all phases of latency. More specifically, as a result of their single reporter design, models cannot differentiate between initial (direct) and secondary (progressive) HIV-1 silencing. Canonically, HIV-1 latency is thought to be the result of progressive epigenetic silencing of previously active infections. However due to the limitations of current latency models, the role of direct silent-infections in general HIV-1 latency has been largely underappreciated. Some evidence suggesting that ‘silent infections’ may play a role in HIV latency (Duverger et al., 2009); however, the frequency and regulation of these infections remains largely unknown. In order to better study HIV-1 early latency establishment and detect LTR-silent infections, we constructed and characterized a ‘double-labeled’, single-cycle HIV-1 vector suitable for studying HIV-1 transcription at the single cell level (Red-Green HIV-1, RGH). We show that in infected Jurkat cells, a substantial proportion of infections (approximately 65%) are LTR-silent very early after infection. This LTR-silent population is transcriptionally competent and can be re-activated by several HIV-1 activating compounds. In addition, we show that the frequency of early silent infections is variable with respect to specific HIV-1 subtype LTRs. In summary, the series of RGH vectors described here provides a new model system to effectively study different aspects of HIV-1 latency at the single cell level without the need for selection steps or extensive culturing.  !  29  2.2 Experimental procedures 2.2.1 Construction of a panel of red-green-HIV-1 (RGH) vectors To construct the RGH molecular clone, an ApaI/BssHII fragment containing gagenhanced Green Fluorescent Protein (eGFP) was cloned from the Gag-iGFP NL4-3 clone (Hübner et al., 2007) into pLAI (Peden et al., 1991) to create pLAIGag-iGFP. Of note, the Envelope open reading frame was disrupted by the introduction of a frameshift at nucleotide 7136 by digestion with KpnI, blunting with Klenow, and re-ligation. The CMV-mCherry (Shaner et al., 2004) cassette was PCR amplified from pcDNA3.1+-mCherry and cloned into pLAI-Gag-iGFP using the BlpI/XhoI sites to create the final double-labeled construct. The ΔU3 RGH clone was created by cloning a ΔU3 linker from pTY-EFeGFP (Chang et al., 1999; Iwakuma et al., 1999; Cui et al., 1999; Zolotukhin et al., 1996) into the KpnI/SacI sites of the 3’ LTR in the double-labeled clone. The ΔCMV RGH clone was created by removing a BsmBI/PmeI fragment containing the CMV promoter from the double-labeled construct. The Integrase mutant D116A was created by two-step PCR mediated site directed mutagenesis and cloning the final amplicon into the ApaI/SalI sites of the RGH construct. The group M subtype promoters (N = 7; B, A, C, D, AE, F, G) were cloned into the RGH construct by transferring a HindIII/XhoI fragment from subtype constructs (Jeeninga et al., 2000) to the 3’ LTR of the RGH clone. The oligonucleotides and plasmids used in this chapter are listed in Tables 2.1 and 2.2, respectively Table 2.1 Oligonucleotides used in Chapter 2. Name oMD090 oMD092 oMD204 oMD205 oMD206 oMD208 oMD209 oMD229 !  Sequence GAGACGCTGCAGACCCGCGACGGGCCAGATATACGCGTTGAC AAAAAAACTCGAGCCTACTTGTACAGCTCGTCCATGC GCTATGTCGACACCCAATTCTG CAATACATACAGCCAATGGCAGCAATTTC GAAATTGCTGCCATTGGCTGTATGTATTG AAAAAAGCTGAGCCGGGGTTGGGGTTGCGCCTTTTC AAAGTTTAAACCTGGGGAGAGAGGTCGGTGATTCG CAAAAATTGCAGGGCCCCTAGG 30  Table 2.2 Plasmids used in Chapter 2.  Name pLAI pLAI-ΔEnv pGag-iGFP pmCherry pCDNA3.1+ pLAI-Gag-iGFP pHIV-luc(B) pHIV-luc(A) pHIV-luc(C1) pHIV-luc(D) pHIV-luc(E) pHIV-luc(F) pHIV-luc(G) pTY-EFeGFP pHEF-VSVg pDblLabel-WT pDblLabel-ΔU3 pDblLabel-ΔCMV pDblLabel-IntD116A pDblLabel-SubA pDblLabel-SubC1 pDblLabel-SubD pDblLabel-SubAE pDblLabel-SubF pDblLabel-SubG  !  Description LAI molecular clone LAI with Env frameshift NL4-3 gag-iGFP clone mCherry coding sequence CMV expression vector LAI gag-iGFP clone LTR subtype B - Luciferase LTR subtype A – Luciferase LTR subtype C1 – Luciferase LTR subtype D – Luciferase LTR subtype E – Luciferase LTR subtype F – Luciferase LTR subtype G – Luciferase SIN HIV-1 lentiviral vector VSV-g envelope expression vector Dbl Labeled clone Dbl Labeled ΔU3 3’ LTR clone Dbl Labeled ΔCMV clone Dbl Labeled D116A Int clone Dbl Labeled subA 3’ LTR clone Dbl Labeled subC1 3’ LTR clone Dbl Labeled subD 3’ LTR clone Dbl Labeled subAE 3’ LTR clone Dbl Labeled subF 3’ LTR clone Dbl Labeled subG 3’ LTR clone  Reference (Peden et al., 1991) This study (Hübner et al., 2007) (Shaner et al., 2004) Invitrogen This study (Jeeninga et al., 2000) (Jeeninga et al., 2000) (Jeeninga et al., 2000) (Jeeninga et al., 2000) (Jeeninga et al., 2000) (Jeeninga et al., 2000) (Jeeninga et al., 2000) (Iwakuma et al., 1999) (Chang et al., 1999) This study This study This study This study This study This study This study This study This study This study  31  2.2.2 Cell culture, virion production, and transduction Jurkat E6-1 (Weiss et al., 1984), SupT1 (Smith et al., 1984), U937 (ATCC), HeLa (ATCC), and HEK293T (ATCC) cells were cultured according to standard conditions as previously described (Dahabieh et al., 2011). VSV-G pseudotyped viral stocks were created by co-transfecting HEK293T cells with viral molecular clones and pHEF-VSVg (Chang et al., 1999) in a 10:1 ratio, respectively, as previously described (Dahabieh et al., 2011). Viral titers were determined by infection of TZM-bl reporter cells as reported previously (Harari et al., 2009). Jurkat E6-1 cells were transduced with viral stocks by spinoculation as previously described (O’Doherty et al., 2000), except that 5x105 cells were spinoculated for 1.5 hours in 12 well plates in 1 mL complete media supplemented with 4 μg/mL polybrene (500 x g, room temperature). Virus was added to the cells at an MOI of ~0.2, such as to yield an average infection rate of 10% in order to ensure single copy integrations. Unless otherwise indicated, infected cells were utilized for experiments at 4 days post infection. 2.2.3 Flow cytometry and drug treatments Prior to analysis, cells were fixed in 1% (v/v) formaldehyde for 10 minutes at room temperature. Cells were analyzed on a Becton Dickinson LSRII flow cytometer and the data was analyzed using FlowJo. Statistical tests (Student’s T-test) were performed using R 2.15.1. Cells were treated with various drugs for the times and durations indicated in individual experiments. Drugs were added at the listed concentrations to complete media. Unless otherwise stated, drugs were used at the following concentrations: Raltegravir, 10 μM; TNFα, 10 ng/mL, SAHA, 1 μM (Marks, 2007); TSA, 50 ng/mL; PMA, 4 ng/mL; Ionomycin, 1 μM; 5-aza-dC, 5 μM.  !  32  2.3 Results 2.3.1 Characterization of the double-labeled HIV-1 molecular clone RGH We constructed a HIV-1 LAI molecular clone containing two fluorescent markers to identify both silent and productive HIV-1 infection (Figure 2.1A). The eGFP protein is flanked by HIV-1 protease cleavage sites and placed in gag between matrix and capsid (Hübner et al., 2009, 2007). Each Gag protein contains eGFP resulting in green virions, which upon fusion with the target cell, release eGFP into the target cell (Figure 2.1B). Upon integration, Gag-eGFP expression is under control of the HIV-1 promoter and may serve as a quantitative marker for HIV-1 LTR gene activity. Moreover, Gag-eGFP is a late gene product and, hence, is indicative of HIV-1 virus production. The second reporter, inserted in the viral nef position, consists of a cassette containing the CMV immediate-early promoter expressing mCherry fluorescent protein (Shaner et al., 2004). mCherry is constitutively expressed and allows for detection of integrated virus regardless of the transcriptional state of the HIV-1 LTR. The combination of the two fluorescent proteins under the HIV-1 LTR and an internal CMV promoter (Red-Green-HIV-1, RGH) allows for quantitative detection of active virus production (GFP+ and mCherry+, yellow), but more importantly, also detects non-productive HIV-1 (GFP- and mCherry+, red, Figure 2.1C). In addition, envelope is inactivated, which prevents spreading viral replication, super-infection, as well as envelope induced/associated cell toxicity. The deletion in envelope also allows us to circumvent the use of antiretroviral drugs in cultures, which would be aimed at preventing virus spread.  !  33  A  Red-Green HIV-1 (RGH) - WT 5’ LTR  gag MA  U3 R U5  CA  NC  vif pol  tat vpu  Δenv  Jurkat cells  RGH  nef  * env  vpr  eGFP  B  3’ LTR  rev  CMV  U3 R U5  mCherry  C FACS Gating  Integrated RGH  LTR+ CMV+  mCherry eGFP (virion)  eGFP (de novo)  Time  CMV - mCherry  Fluorescence  Uninfected  ‘Red’ LTRCMV+  ‘Yellow’ LTR+ CMV+  ‘Neg.’ LTRCMV-  ‘Green’ LTR+ CMV-  Integrated RGH  LTR- CMV+  LTR - eGFP  Figure 2.1 Schematic representation of the Red-Green-HIV-1 (RGH) vector. A: HIV-1 B-LAI Δenv is labeled with eGFP as an in-frame gag fusion flanked by HIV-1 protease cleavage sites (inverted triangles), and by a CMVIE-driven mCherry cassette located in place of nef. B: Schematic depiction of RGH infection of target cells and the resultant fluorescent protein profiles over time. C: Schematic depiction of HIV-1 RGH infected cell populations detected by FACS.  !  34  2.3.2 Red-green-HIV-1 identifies LTR-silent infections, established early after infection and require integration  which  are  To test the double-labeled system (Figure 2.1), we produced VSV-G pseudotyped Red-Green-HIV-1 viral stocks in HEK293T cells and infected Jurkat T-cells at low MOI (~0.2). At one-day post infection distinct green, yellow and red populations were observed, but only the yellow and red populations, representing integrated proviruses, persisted through later time points (Figures 2.2A and 2.2B). The number of LTR-silent infected cells (red) consistently outnumbered the double positive HIV-1 producing cells (yellow). Interestingly, day one post infection was characterized by a high number of GFP+ mCherrycells, which were no longer observed at later time points (Figures 2.2A and 2.2B). This transient eGFP+ cell population is the result of the Gag-eGFP fusion proteins incorporated in the virus used to infect the cells, and hence, rapidly disappears after infection (Figures 2.1B and references 24, 25). We calculated the ratio of red to yellow cells for all time points (latency ratio), and observed a ratio spike two days post infection caused by a high percentage of red cells over yellow cells (Figure 2.2B) possibly caused by delayed late Gag-eGFP expression compared to more rapid mCherry accumulation. Consistent with this, the redyellow ratio (latency ratio) dropped at later time points and stabilized approximately 4-5 days after infection (Figure 2.2C). Non-integrated 2-LTR circles have been described to express several HIV-1 non-structural proteins, such as Tat, Rev and Nef (Gillim-Ross et al., 2005; Gelderblom et al., 2008). To exclude that the higher mCherry signal is a result of expression from 2-LTR circles we infected Jurkat cells with RGH virus in the presence of integrase inhibitor raltegravir (Figure 2.2D) or with an RGH virus bearing a D116A inactivating mutation in Integrase (Figure 2.2E, ref 4). Both raltegravir and the Integrase mutant prevented the formation of red and yellow cells (Figures 2.2D and 2.2E), indicating that the red and yellow signals observed in RGH infected cells are a result of integrated virus and not 2-LTR circles. The eGFP expression at day one post infection, in contrast, was not affected by raltegravir or the integrase mutation underscoring that the initially observed eGFP peak is caused by the virally packaged eGFP and not from newly expressed eGFP in the target cells. Collectively, these data indicate that the RGH Jurkat cell infection system can efficiently discriminate between active and silent HIV-1 expression early upon integration.  !  35  A  RGH infected Uninfected  CMV - mCherry  105  10  1 day p.i.  0  0 105 0.787  4  10  4 days p.i. 0.666 105 3.96  4  10  7 days p.i. 2.14 105 2.42  4  10  4  103  103  103  103  102  102  102  102  0  0  0  100 0  0.0102 10  2  10  3  10  4  10  5  82.1 0  10  16.4 2  10  3  10  4  10  93.8  5  0  10  0.0861 2  10  3  10  4  10  1.5  0  96  5  0  0.0815 10  2  10  3  10  4  10  5  LTR - eGFP B  C  RGH - WT eGFP+ mCherryeGFP- mCherry+ eGFP+ mCherry+  10 8 6 4  *  4  *  3  ns  2 1  2 0  0 0  1  2 3 4 5 Days post infection  6  7  D  0  1 2 3 4 5 6 Days post infection  7  E 16  16  RGH - WT + Raltegravir  14 12 10 8 6 4 2  RGH - Integrase D116A  14 % Positive cells  % Positive cells  5  Yellow  12  6 Red-Yellow Ratio  % Positive cells  14  Red  7  16  12 10 8 6 4 2  0  0 0  1  2 3 4 5 Days post infection eGFP+ mCherry-  6  7  eGFP- mCherry+  0  1  2 3 4 5 Days post infection  6  7  eGFP+ mCherry+  Figure 2.2 LTR-silencing in RGH infected Jurkat cells occurs early and requires integration.  !  36  A: Flow cytometry time course of RGH infected Jurkat cells. Plots shown are representative of triplicate infections. B: Plot of positive cells from each colored fraction (eGFP+ mCherry-, eGFPmCherry+, eGFP+ mCherry+) over the course of infection. Error bars represent standard deviations of triplicate experiments. C: Data from the experiment shown in panel B is enumerated as the ratio of red to yellow cells (proportion of eGFP- mCherry+ to eGFP+ mCherry+). Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant, * p<0.05. D: Jurkat cells were infected with RGH in the presence of the integrase inhibitor raltegravir (10 μM). Cells were analyzed by flow cytometry over a period of seven days. Error bars represent standard deviations of triplicate experiments. E: Jurkat cells were infected as in panel D, except that an RGH variant bearing a catalytically defective Integrase variant (D116A) was used. Additionally, raltegravir was excluded.  2.3.3 Identification of LTR-silent infections is dependent on the presence of both LTR and CMV promoters Infection of Jurkat cells with Red-Green-HIV-1 indicates that the majority of integrated proviruses are LTR-silent four days post infection (Figure 2.2). To characterize the effect of both the LTR and CMV promoters on this infection profile, we constructed a number of control RGH variant viruses that lack either CMV-mCherry, the CMV promoter, or the HIV-1 promoter (Figure 2.3A). As expected, CMV-mCherry deleted virus only produced green cells (Figure 2.3B). RGH lacking the U3 promoter region of the 3’ LTR results in an integrated virus devoid of the 5’ HIV-1 promoter, and results in red only cells (Figure 2.3B). Finally, the deletion of the CMV promoter in RGH renders mCherry dependent on the HIV-1 promoter and results in the simultaneous expression of eGFP and mCherry, resulting exclusively in double positive, yellow cells (Figure 2.3B). Inspection of wild-type RGH infected Jurkat cells by fluorescence microscopy showed two distinct cell populations; cells expressing both fluorescent proteins (eGFP+ mCherry+, ‘yellow’) and cells expressing only mCherry (eGFPmCherry+ ‘red’) (Figure 2.3C). Taken together, these data confirm that having a virus with independent expression of both fluorescent reporters is necessary to detect early LTR-silent infections.  !  37  We next wanted to determine if the LTR-silent infections were specific for Jurkat T-cells or a bona fide property of HIV-1 infection. To do this, we infected different human cell lines including the T-cell line SupT1, the monocyte line U937, and the non-immune cell lines HEK293T and HeLa, with the RGH viral vector stock. Cell lines differed in susceptibility to infection (between 5 and 37% infection, Figure 2.3D) but most infected cell lines yielded at least twice as many red cells as red-green (yellow) double positive cells (SupT1: red only: 24.5%, yellow: 12.3%, ratio: 1.99; U937: red only: 5.5%, yellow: 0.7%, ratio: 7.85; HEK293T: red only: 6.8%, yellow: 1.9%, ratio: 3.58; HeLa red only: 2.8%, yellow: 2.5%, ratio: 1.12). These results indicate that LTR-silent infections are common and evident in many different cell types (Figure 2.3D). Collectively, these data suggest that the independent expression of both fluorescent reporters is necessary to detect early LTR-silent infections, and that these infections are a bona fide property of HIV-1 infection.  !  38  RGH Variant  A  CMV-mCherry  gag-eGFP  CMV  U3 R U5 5’ LTR  WT  U3 R U5 3’ LTR  Δenv gag-eGFP  U3 R U5 5’ LTR gag-eGFP  Δenv  RGH - WT CMV - mCherry  9.43  gag-eGFP 5.06 105 0  RGH - ΔCMV  0.0486 105 0.327  104  104  104  103  103  103  103  102  102  0.217  85.3 0  ΔCMV  RGH - ΔU3 (3’) 0 105 3.58  104  0  U3 R U5 3’ LTR  mCherry  U3 R U5 5’ LTR  105  ΔU3 (3’)  Δenv gag-eGFP  B  R U5 3’ LTR  CMV  U3 R U5 5’ LTR  102  103  104  gag-eGFP  U3 R U5 3’ LTR CMV-mCherry  Δenv  0  102  91.1  105  0  102  8.87 103  104  0  8.48  102  96.3  105  0.0583  102  0  103  104  0  88.6  105  0  2.6  102  103  104  105  LTR - eGFP C  D Jurkat – RGH infected (4 days p.i.) mCherry  DAPI  SupT1 105  104  103  103  102  102  50 µM 105  50 µM  102  103  104  0  105  6.78  0  102  103  103  102  102  91.3 102  104  1.85 105 2.78 104  0  0.0243 103  0.11 103  104  105  HeLa  104  0  0.657  93.8  HEK293T  merge  50 µM  0.364  62.9 0  CMV - mCherry  eGFP  U937 12.3 105 5.51  104  0  50 µM  24.5  105  0  2.45  94.7 0  102  0.122 103  104  105  LTR - eGFP  Figure 2.3 Identification of LTR-silent RGH infections is dependent on both the LTR and CMV promoters, and is not specific for Jurkat T cells specific. A: Schematic representation of RGH variant viruses. Gag-eGFP lacks the CMVmCherry construct, while ΔU3 and ΔCMV both contain both fluorescent coding !  39  sequences (eGFP and mCherry) but lack U3 in the 3’ LTR, and the CMV promoter, respectively. Variants are otherwise isogenic. B: Jurkat cells were infected with WT and variant RGH and analyzed by flow cytometry four days post infection Plots shown are representative of triplicate experiments. C: Fluorescence microscopy (100x) of RGH infected Jurkat cells (4 days post infection). eGFP- mCherry+ and eGFP+ mCherry+ cells are indicated in the merge panel by red and yellow arrows, respectively. Images shown are representative of triplicate experiments. D: SupT1, U937, HEK293T, and HeLa cells were infected with RGH and analyzed by flow cytometry four days post infection Plots are representative of duplicate experiments.  2.3.4 Silent LTRs in infected cells are transcriptionally competent The HIV-1 LTR contains binding sites for a multitude of transcription factors (such as NFκB, NFAT, and AP-1) that act downstream of the T-cell signalling pathway, and hence, can be activated by a variety of signalling agonists. For example, Tumor necrosis factor alpha (TNFα), and phorbol 12-myristate 13acetate (PMA) / Ionomycin (Iono) are both potent activators of HIV-1 by stimulating the protein kinase C (PKC), and MAP kinase / Calcinurin pathways, respectively (Folks et al., 1988, 1989). Additionally, general epigenetic modifying drugs like the HDAC inhibitors SAHA and Trichostatin A (TSA) cause hyperacetylation of LTR nucleosomes and activation of LTR-driven transcription (Contreras et al., 2009; Archin et al., 2009). The DNA methyltransferase inhibitor 5-aza-2′deoxycytidine (5-aza-dC) has been shown to activate HIV-1 transcription, although its effects depend on the model system used (Fernandez and Zeichner, 2010). We next determined whether the majority of RGH infected Jurkat cells, which are LTR-silent four days after infection (Figure 2.2), could be reactivated. We treated RGH infected Jurkat cells four days post infection with TNFα, PMA, Iono, PMA/Iono, 5-aza-dC, SAHA, TSA, TNFα/SAHA or DMSO (negative control) for 24 hours followed by FACS (Figures 2.4A and 2.4B). TNFα, PMA, PMA/Iono, SAHA, TSA and TNFα/SAHA activated silent LTRs as compared to the DMSO control (approx. 3-fold decrease in red-yellow ratio, Figures 2.4A and 2.4B). Interestingly, 5-aza-dC failed to activate silent LTRs (Figures 2.4A and 2.4B). It has been proposed that DNA methylation is a late mark in HIV-1 proviral silencing and, thus, 5-aza-dC would not be expected to play a role at an early time point after integration (Kauder et al., 2009). Additionally, the combination of !  40  TNFα and SAHA did not have an additive effect on LTR activation, suggesting that maximal effect had been reached with either drug alone (Figures 2.4A and 2.4B). For all treatments that activated silent RGH, we consistently noticed a decrease in the negative cell population (eGFP- mCherry-, Figures 2.4A and 2.4B), which suggests that the double-negative population may also harbor infected cells with inactivated LTR and CMV promoters. These data indicate that LTR-silent RGH infections in Jurkat cells are transcriptionally competent, and can be reactivated by a variety of T-cell signalling agonists and HDAC inhibitors.  !  41  A DMSO 105  TNFα  4.04  PMA/Iono  11.6 105 8.7  10 105 6.4  104  104  104  104  103  103  103  103  102  102  102  102  0  0  0  93.2 0  0.101  102  103  104  105  81.7 0  102  105  4.52  0.244 103  104  105  81 0  0.291 102  SAHA  5-aza-dC CMV - mCherry  PMA  2.64 105 6.46  103  104  0  0  104  104  103  103  103  103  102  102  102  102  0  0  0  0.101  102  103  104  105  84.4 0  102  0.242 103  104  105  0.368  82.3 0  0.326 103  104  12.1 105 6.25  10.1 105 5.15  104  0  102  102  103  105  TNFα / SAHA  104  92.4  80.7  105  TSA  2.96 105 5.25  12.6  104  0  14.2  79  105  0  0.499 102  103  104  105  LTR - eGFP B 2  Red  ns  1.5  *** 0.5  FACS (4 d post infection)  **  **  1  ***  ***  ** Yellow  Drug treatment (3 d post infection)  Red-Yellow Ratio  Infection  0  Figure 2.4 Silent LTRs in RGH infected cells are transcriptionally competent. A: RGH infected Jurkat cells (4 days post infection) were treated with DMSO, TNFα, PMA, PMA/Iono, 5-aza-dC, SAHA, TSA, or TNFα/SAHA for 24 hours prior to analysis by flow cytometry. Plots shown are representative of triplicate experiments. B: Data from panel A is enumerated as the red-yellow ratio of the infected population. Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant, ** p<0.01, *** p<0.001 (Student’s T-test).  !  42  2.3.5 Red-green-HIV-1 recapitulates differences in latency observed between group M subtypes Given that the RGH vector can detect LTR-silent infections in Jurkat cells (Figures 2.2, 2.3, and 2.4), we reasoned that the ratio of LTR-silent red cells to LTR-active yellow cells would likely be affected by intrinsic determinants within the HIV-1 LTR, such as transcription factor binding sites. Therefore, we cloned natural subtype LTR variants (Jeeninga et al., 2000) into the RGH vector and assessed the effect of LTR variation on silent-infections and reactivation potential. Jurkat cells were infected with each of the RGH subtype LTR viruses (subtypes B, A, C, D, AE, F and G, Figure 2.5A) at low MOI and treated with PMA/Iono or DMSO four days post infection to activate LTR-silent RGH (Figures 5B and 5C). After PMA/Iono activation, the red-yellow ratio dropped substantially (approx. 2-4 fold) for most subtypes compared to control DMSO treatment. However, subtype AE exhibited the lowest red-yellow ratio difference between DMSO and PMA/Iono treatment, suggesting that the subtype AE promoter is constitutively expressed and, thus, cannot be further activated by PMA/Iono (Figures 2.5C and 2.5D). Since the subtype AE promoter only contains one canonical NFκB binding site (Jeeninga et al., 2000; van der Sluis et al., 2011), limited activation by the PMA/Iono treatment could be the underlying reason (Figures 2.5C and 2.5D). Interestingly, the fold change in latency ratios is significantly higher for subtype D and F LTRs suggesting that these promoters are more prone to transcriptional silencing in Jurkat T cells (Figure 2.5D). Of note, for subtype AE the fold difference in latency ratios between DMSO and PMO/Iono treatment is substantially lower compared to B-LAI (Figure 2.5D). Our results suggest that different subtypes display different levels of LTR silencing. Subtype AE, contrary to the other subtypes, is less likely to become silenced, which is in full agreement with previous reports (Jeeninga et al., 2000; van der Sluis et al., 2011) while subtypes D and F are more prone to silencing. Using the RGH Jurkat cell model, we observed differences in the occurrence of LTR-silent infections between subtypes, which recapitulated well the results observed in earlier studies using different models to study HIV-1 latency.  !  43  A  gag-eGFP  CMV-mCherry  5’ LTR MA  U3 R U5  Group M subtypes  CA  NC pol  vif  tat vpu vpr  B-LAI  TTCAA  A  TACAA  AP1  C  TACAA  NFκB NFκB  SP1x3 TATAA  AP1 NFκB NFκB  SP1x3 TATAA  AP1 NFκB NFκB NFκB  SP1x3 TATAA  D  TACAA TATAA  AP1  GABP  F  TACCA  AP1  AP1 NFκB NFκB  SP1x3 TATAA  G  TACAA  AP1  NFκB NFκB  SP1x3 TATAA  NFκB NFκB  B 2.98  1.71  0.732 105 1.65  104  104  104  103  103  103  0  0.0523  102  103  104  0  105  95.2 0  102  0.444  102  97.5  105  0  102  AE  102  CMV - mCherry  102  97.4 0  3  0  D  0.821 105 1.71  102 104  SP1x3 TAAAA  C  1.69  Core promoter elements  SP1x3 TATAA  NFκB  A 105  10  U3 R U5  Δenv  AE  B  105  3’ LTR  rev  0.0523 103  104  0  105  97.9 0  0.0209  102  103  G  2.21  104  105  F  1.35 105 2.49  1.11 105 1.27  0.704  0.0847 103  104  105  104  104  104  103  103  103  102 0  102  96.4 0  0.079  102  103  104  0  102  96.3  105  0  102  0.0477 103  104  0  105  98 0  0.0315 102  103  104  105  LTR - eGFP D  C D AE LTR subtype  F  G  2 1  F  ***  1.5  G  B-LAI A  ns  2.5  D  0  ***  ns ns  3  AE  1  3.5  A  2  ***  4  C  3  4.5  B-LAI  Red  DMSO PMA/Iono  Yellow  Red-Yellow Ratio  4  Fold change R-Y Ratio (PMA/DMSO)  C  LTR subtype  Figure 2.5 RGH recapitulates differences in latency observed between HIV-1 Group M subtypes. A: Schematic representation of RGH variants containing promoter sequences from the major group M viral subtypes. Viruses are isogenic except for a 209 bp !  44  BseAI/AflII fragment containing the core promoter region of the LTR that stretches from position -147 to +68. B: Jurkat cells were infected with the subtype RGH variants and analyzed by flow cytometry four days post infection. Plots shown are representative of triplicate experiments. C: Jurkat cells were infected with the subtype RGH variants. Four days post infection cells were treated with either DMSO or PMA/Ionomycin for 24 hours, and then analyzed by flow cytometry. Error bars represent standard deviations of triplicate experiments. D: Data from panel C is enumerated as fold change between the PMA and DMSO treatments. Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant, *** p<0.001 (Student’s T-test).  !  45  2.4 Discussion A better understanding of the dynamics of HIV-1 latency and the cellular mechanisms controlling these processes is instrumental to developing HIV-1 eradication strategies. In this study, we constructed and characterized a novel double-labeled HIV-1 vector (Red-Green-HIV-1, RGH), which reduces the selection bias inherent to traditional single-label models of latency. Additionally, this model allows for examination of several phases of HIV-1 latency and reactivation including establishment, maintenance, and expression. By incorporating the CMV-mCherry reporter, the RGH model can positively identify latently infected cells rather than utilizing excluding selection steps, which is a common denominator for many other latency systems. Moreover, utilization of CMV-mCherry allows for the distinction of latently infected cells from uninfected ones, thereby allowing transcriptome and proteome comparisons, without the need for viral reactivation by T cell activation. Reactivation likely destroys the unique transcriptional properties of latently infected cells, thus preventing meaningful comparisons of the viral and cellular states of both latently and actively infected cells. Thus with the RGH vector, all populations of infected cells, including those with initially suppressed LTRs, can be studied and quantified at both the single-cell and population levels. Additionally, the RGH vector may also be used to study canonical mechanisms of HIV-1 latency by continual passaging. These mechanisms, which contribute to the progressive epigenetic silencing of active infections, include both cis-acting factors (e.g. HIV-1 integration site and chromatin environment) and trans- acting factors (e.g. cellular activation state and transcription factor pools) (Siliciano and Greene, 2011; Colin and Van Lint, 2009). The results of the RGH infections (Figures 2.1 and 2.2) suggest that transcriptionally competent, LTR-silent infections are common in T cells as well as other cell types (Figures 2.3 and 2.4). It appears that direct ‘silent-infections’ have been underestimated so far, and it is likely that these cells contribute to HIV-1 latency. To our knowledge, only one other study has identified silentinfection as a contributor to HIV-1 latency. In this study, which used a singlylabeled model of latency, silent infections were correlated with NFκB levels in cells at the time of infection, with transcriptional interference from host genes being the actual silencing mechanism (Duverger et al., 2009). Further studies are needed to definitely address whether control of latency by NFκB is indeed causative, and if this mechanism applies to all sites of viral integration.  !  46  We and others have noted that the recombinant HIV-1 subtype AE is less prone to the formation of latency (Figure 2.5 and Jeeninga et al., 2000; van der Sluis et al., 2011). The LTR of subtype AE contains only one NFκB site and has a binding site for the Ets/MAPK responsive transcription factor GABP (Rosmarin et al., 2004). If early NFκB occupancy at the HIV-1 LTR were important for regulating latency, this would provide an explanation for why the AE promoter is less likely to become latent. However, further studies are required to fully characterize the mechanisms of establishment of post-integration HIV-1 latency and the contribution of LTR-silent infections to the long-lived HIV-1 reservoirs. Interestingly, treatment of RGH infected cells with signaling agonists also revealed that approximately 12% of cells in the negative population (initially eGFP- mCherry-) were actually infected (Figure 2.4). Thus in these cells, both the LTR and CMV promoters formed latency upon infection. These double-silent infected cells may be caused by a variety of factors including integration site effects and/or levels of cellular transcription factors sufficiently low enough to silence both promoters. However, further work is needed to further characterize the occurrence of these infections, and elucidate the cause of this phenotype. Differential mechanisms that lead to silent-infection or silencing from canonically formed latency may require unique pharmaco-therapeutics to reactivate latent proviruses. One of the most commonly proposed eradication strategies, called “shock and kill”, would utilize small molecules to induce HIV-1 expression from latently infected cells, thus allowing induced cells to be cleared by the immune system or killed by viral cytopathic effects (Choudhary, 2011). However, any efficacious drug suitable to purge HIV-1 latency would have to target both forms of latent infection, as excluding any part of the latent reservoir would render such therapy less effective. Given its capacity to minimize selection bias and include all populations of infected cells, the RGH vector model will be a very useful tool to screen for latency modulating drugs that fulfill these criteria.  !  47  2.5 Chapter acknowledgements We thank Andy Johnson and Justin Wong of the UBC Flow Cytometry Facility for assistance with FACS. We also gratefully acknowledge Ben Berkhout for providing the molecular clone pLAI and HIV-1 subtype constructs, and Benjamin Chen for providing the molecular clone Gag-iGFP. We thank Vicente Planelles for critical review of the manuscript and helpful suggestions. We also thank Jacob Hodgson, Adam Chruscicki, Kevin Eade, Benjamin Martin, and Nicolas Coutin for helpful discussion throughout this project. The following reagents were obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH: Jurkat Clone E6-1 from Dr. Arthur Weiss, Sup-T1 from Dr. James Hoxie, Raltegravir (Cat # 11680) from Merck & Company, Inc, SAHA (Vorinostat), pHEF-VSVG from Dr. Lung-Ji Chan, and pTY-EFeGFP from Dr. Lung-Ji Chang. This work was supported by Canadian Institute of Health Research (CIHR) grants to I.S. (MOP-77807, HOP-120237), and NIH/NIAID grants to V.S. (AI064001, AI089246, and AI90935). M.S.D is supported by a CIHR fellowship (CGD-96495).  !  48  3 HIV-1 latency is established early post infection and is regulated by NFκB 3.1 Introduction In Chapter Two, we generated and characterized a comprehensive panel of single cycle vectors to study the very early events involved in establishment of HIV-1 latency. Our results suggest that the proviral LTR is silenced immediately upon integration in a high proportion of infections. HIV-1 latency, thus, may not simply be the result of progressive epigenetic silencing of active infections but rather a multi-faceted process that can form very early post integration and in the absence of previous LTR activity. This underappreciated, and previously hard to measure, population of initially latent cells may have profound consequences for the establishment and treatment of latency. A better understanding of the mechanisms controlling the fate of integrated proviruses will provide novel avenues towards improved treatment strategies. In Chapter Three, we aimed to dissect the mechanisms of direct LTR-silencing observed in the RGH latency model. High throughput sequencing of viral integration sites suggests that the genomic site is not a major determinant of early LTR silencing. Instead, LTR-silent infections appear to be predominantly a function of cellular activation state and NFκB levels at the time of infection. Latency in the RGH system can be modulated by treatment with TNFα only during a defined period post infection (before 4 days post infection). Moreover, direct modulation of NFκB levels during this window period suggests that NFκB is indeed necessary and sufficient to drive direct LTR-silencing in the RGH model. Taken together, this and other evidence implicates the cellular levels of NFκB as the primary determinant of proviral latency early after infection.  !  49  3.2 Experimental procedures 3.2.1 Vectors and constructs To construct the gag-N74D RGH clone, the mutation was created by PCR mediated site directed mutagenesis and cloning of the amplicon into the BspQI/ApaI sites of the previously described RGH construct (Chapter 2). pTRIPz-DN-IκB is a derivative of the commercial doxycycline-inducible lentiviral vector pTRIPz-Ctrl that contains the S32A/S36A mutant version of the IκB repressor PCR amplified from pSVK3-IKBa-2N (Kwon et al., 1998) and cloned into the AgeI/MluI sites of pTRIPz. The oligonucleotides and plasmids used in this chapter are listed in Tables 3.1 and 3.2, respectively. Table 3.1 Oligonucleotides used in Chapter 3. Name oMD232 oMD233 oMD234 oMD235 oMD236 oMD237 oMD238 oMD239 oMD240 oMD241 oMD242 oMD245 oMD246 oMD247 !  Sequence AGAGATGGGTGCGAGAGC ATTAACTGCGAATCGTTCTAGC GTAATACGACTCACTATAGGGCTCCGCTTAAGGGAC [Phos]TAGTCCCTTAAGCGGAG[AmC7-Q] CTTAAGCCTCAATAAAGCTTGCCTTGAG GTAATACGACTCACTATAGGGC CGTATCGCCTCCCTCGCGCCATCAGAGGGCTCCGCTTAAGGGAC CTATGCGCCTTGCCAGCCCGCTCAGACGAGTGCGTAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGACGCTCGACAAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGAGACGCACTCAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGAGCACTGTAGAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGATCAGACACGAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGATATCGCGAGAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGCGTGTCTCTAAGACCCTTTT 50  Name oMD248 oMD249 oMD250 oMD251 oMD252 oMD253 oMD254 oMD255 oMD256 oMD263 oMD264 oMD265 oMD266 oMD817 oMD818  !  Sequence AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGCTCGCGTGTCAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGTAGTATCAGCAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGTCTCTATGCGAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGTGATACGTCTAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGTACTGAGCTAAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGCATAGTAGTGAGACCCTTTT AGTCAGTGTGGAAAATC CTATGCGCCTTGCCAGCCCGCTCAGCGAGAGATACAGACCCTTTT AGTCAGTGTGGAAAATC AAAACCGGTATGTTCCAGGCGGCCGAGC AAAACGCGTTCATAACGTCAGACGCTGGC GATAAGATAGAGGAAGAGCAAAAC GTTTTGCTCTTCCTCTATCTTATC TAAAAGAGACCATCGATGAGGAAGCTGCAGAA TTCTGCAGCTTCCTCATCGATGGTCTCTTTTA AAAACTCGAGCCTACTTGTACAGCTCGTCC AAAGGATCCATGGTGAGCAAGGGCGAGGAG  51  Table 3.2 Plasmids used in Chapter 3.  Name pHEF-VSVg pcDNA3.1-Tat pLP1-gag/pol pLP2-Rev pSVK3-IKBa-2N pTRIPz-control pTRIPz-DN-IκB pDblLabel-WT pDblLabel-gagN74D  Description VSV-g envelope expression vector HIV-1 Tat expression vector HIV-1 gag/pol expression vector HIV-1 Rev expression vector S32A/S36A IκB clone Doxycycline inducible Lentiviral expression vector S32A/S36A IκB expression vector Dbl Labeled clone Dbl Labeled N74D gag clone  Reference (Chang et al., 1999) This study Pauline Johnson Pauline Johnson (Kwon et al., 1998) Thermo Fisher This study This study This study  3.2.2 Cell culture, virion production, and transduction Jurkat E6-1 (Weiss et al., 1984) and HEK293T (ATCC) cells were cultured according to standard conditions as previously described (Dahabieh et al., 2011). VSV-g pseudotyped viral stocks were created by transfecting HEK293T cells with viral molecular clones and pHEF-VSVg (Chang et al., 1999) in a 10:1 ratio as previously described (Dahabieh et al., 2011). Jurkat E6-1 cells were spinoculated as previously described (Chapter 2). Dominant negative IκB was conditionally expressed in Jurkat cells by transducing with pTRIPz-DN-IκB virus at an MOI ~ 4, and selecting with puromycin (1 μg/mL) for one week. pTRIPz-DN-IκB virus was produced in HEK293T cells by co-transfection with pTRIPz-DN-IκB, pHEF-VSVg (Chang et al., 1999), pLP1-gag/pol, pLP2-Rev, and pcDNA3.1-Tat. Virus was concentrated by pelleting into an Optiprep cushion and resuspending in complete media. 3.2.3 Flow cytometry and staining Prior to analysis, cells were fixed in 1% (v/v) formaldehyde for 10 minutes at room temperature. Cells were analyzed on a Becton Dickinson LSRII flow cytometer and the data was analyzed using FlowJo. Live cell sorting was !  52  performed on a Becton Dickinson Influx cell sorter. Statistical tests (Student’s Ttest and one-way-ANOVA) were performed using R 2.15.1. Jurkat E6-1 cells were stained and analyzed for CD69 as previously described (Bernhard et al., 2011) except that antibodies were conjugated to PE-Cy7 and 1 μL of antibody was used per 1x105 cells. Jurkat cells were stained with PE-Cy7-NFκB p65 (pS529) and NFκB p50 with Pacific Blue conjugated secondary antibody as previously described (Grupillo et al., 2011). 3.2.4 Drug treatments Cells were treated with various drugs for the times and durations indicated in individual experiments. Drugs were added at the listed concentrations to complete media. Unless otherwise stated, drugs were used at the following concentrations: TNFα, 10 ng/mL; SAHA, 1 μM (Marks and Breslow, 2007); PMA, 4 ng/mL; Ionomycin, 1 μM. 3.2.5 Pyrosequencing of integration sites HIV-1 integration sites were analyzed by pyrosequencing as previously described (Ciuffi and Barr, 2011). Genomic DNA from 5x105 infected cells was digested with MseI and ligated to adaptors. Nested PCR was performed to amplify the HIV-host genome junctions. After gel extraction of 100-600 bp fragments, amplicons were subjected to pyrosequencing on a 454 GS Junior machine. Data was analyzed using the Integration Site Pipeline and Database (INSIPID) web tool (Bushman Lab - http://microb215.med.upenn.edu/Insipid/ Berry et al., 2006), Circos (Krzywinski et al., 2009), and SeqMonk software. 3.2.6 Chromatin immunoprecipitation (ChIP) ChIP analysis of histone modifications was performed on infected Jurkat E6-1 cells. 1x104 cells were sorted into 50 μL EZ nuclei lysis buffer supplemented with 0.1% (v/v) Triton X-100 and 0.1% (w/v) sodium deoxycholate. Three aliquots of 1x104 cells were pooled and mixed thoroughly by pipetting. Samples were digested with 60 U/mL Micrococcal nuclease (MNase) in MNase reaction buffer (50 mM TRIS, pH 7.8, 5 mM CaCl2, 6.25% PEG 8000, 2 μM DTT) for 5 minutes at room temperature to generate predominantly mononucleosomes. Reactions were stopped with the addition of 10 mM EDTA and lysis was completed by the addition of 0.5% Triton X-100/deoxycholate. Samples were divided into 4 equal aliquots (3 IP + 1 Input) and volumes were brought up to 300 μL by addition of !  53  IP buffer (20 mM TRIS, pH 7.8, 2 mM EDTA, 150 mM NaCl, 0.1% (v/v) Triton X100, 0.1% (w/v) deoxycholate, 1x Protease inhibitor cocktail, 1 μM PMSF). Immunoprecipitaitons were performed with 1 μg of the following antibodies overnight at 4 °C with rotation: ab1791, ab8580, ab4441. Antibody-antigen complexes were captured with 20 μL of Dynabeads-Protein G for 30 minutes at 4 °C with rotation. Beads were washed 3 times with 200 μL of low salt wash buffer (20 mM TRIS, pH 7.8, 2 mM EDTA, 150 mM NaCl, 0.1% (v/v) Triton X100, 0.1% (w/v) deoxycholate, 1x Protease inhibitor cocktail, and 3 times with 200 μL of high salt wash buffer (low salt buffer supplemented with 500 mM NaCl). At the last high salt wash, beads were transferred to new tubes and precipitated material was eluted from the beads by incubation with 80 μL elution buffer (100 mM NaHCO3, 1% (w/v) SDS) for 2 hours at 65 °C. Eluted material was extracted once with Phenol/CHCl3 in phase lock tubes and ethanol precipitated overnight with a linear polyacrylamide carrier. Precipitated DNA was washed once with 70% (v/v) ethanol, air dried, and pellets were resuspended in 20 μL of 10 mM TRIS, pH 7.8. Immunoprecipitated DNA was analyzed by qPCR using iQ SYBR Green Supermix, 2 μL of template DNA, and primers specific for LTR nuc2.  !  54  3.3 Results Using the RGH model system, we have shown that direct LTR-silent infections are very common events representing ~65% of all infections in Jurkat T cells and other cell types. Furthermore, these infections are transcriptionally competent as they can be reactivated by a variety of T cell signalling agonists. We also tested natural HIV-1 LTR subtype variants in the RGH model, and showed that subtypes differ significantly in respect to latency establishment. Collectively, these findings indicate that, contrary to current dogma, latent HIV-1 can be established very early following infection, and without prior LTR activity (Chapter 2). Despite this knowledge, the mechanisms driving the formation of direct LTRsilent infections are currently unknown. Moreover, it remains to be seen how these infections form differentially in an otherwise homogeneous cell population.  3.3.1 The frequency of direct LTR-silent infections is modulated by T cell signalling HIV-1 transcription is more efficient in activated cells as they contain higher levels of the activating forms of various transcription factors (such as NFκB, AP1, NFAT and SP-1) (Reviewed in Colin and Van Lint, 2009). Activated cells also contain higher levels of the active form of the cellular transcription elongation factor pTEF-b (CDK9/CyclinT1) (Reviewed in Karn and Stoltzfus, 2012). Consequently, differences in the level of cellular activation mediate changes in LTR transcriptional output. Given the link between the availability of transcription factors and LTR output, we speculated that cellular factors might be responsible for mediating the LTRsilencing seen in 65% of RGH infected cells (Chapter 2). As differences in T cell activation, and in turn transcription, may be important for the fate of early HIV-1 latency, we examined whether modulating T cell receptor signaling would have an effect on LTR-silencing. We treated Jurkat cells with DMSO, TNFα, or the HDACi SAHA during infection and then with DMSO or PMA/Iono 24 hours prior to analysis at four days post infection. Compared to DMSO, TNFα treatment at the time of infection reduced latency as reflected in 2.5-fold decrease in the latency ratio (Figures 3.1A and 3.1B). Furthermore, TNFα largely prevented LTRsilencing days later as PMA/Iono treatment of these cells prior to analysis had !  55  no further effect in reducing the proportion of latent cells (Figure 3.1A and 3.1B). Interestingly, treatment with SAHA was unable to prevent early latency, as the ratio of red to yellow cells was similar to the DMSO control (Figure 3.1A and 3.1B). This suggests that epigenetic modifications i.e. acetylation are not a major mediator of early latency, consistent with previous reports (Duverger et al., 2009). Moreover, this is expected considering SAHA (and HDACi’s as a whole) are not known to activate T cells but instead modulate the expression of only 10-20% of genes (Van Lint et al., 1996b). Of note, we also observed similar effects with PMA/Iono pre-treatment (Figure 3.1C). Together, these results suggest that early latency is mediated by the action of factors responsive to TNFα and PMA/Iono treatment during infection.  !  56  A  Treatment at time of infection DMSO 105  3.75  SAHA 9.85 105 7 104  103  103  103  102  102  102  0  0  94  105  0.119 102  103  104  7.32  83.6  105  0  102  0  0.181 103  104  104  104  103  103  103  102  102  102  0  0  102  0.253 103  104  85  105  0  0  0.205 102  103  104  105  103  104  11.9  PMA/Iono  104  0  0.195  102  0  8.24 105 11  12.5 105 6.52  79.9  89.5  105  76.8  105  0.317  102  0  Pre-FACS treatment  104  3.36  DMSO  104  0  CMV - mCherry  TNFα 2.16 105 6.37  103  104  105  LTR - eGFP B  C  Red  DMSO PMA/Iono  105  ns  10  9.43  PMA/Iono 5.06  4  10  103  2 1.5  0.5 0 DMSO TNFα SAHA Treatment at time of infection  CMV - mCherry  *** ***  Yellow  ***  102  103  104  5.97  0  105  0  102  103  104  12.8 105 14.5  104  104  103  103  102 0  2.11  67.4  105  17.7  PMA/Iono  1  105  17.1  4  102  0.217  85.3 0  ***  13.3  103  102 0  105  102  1.88  79.4 0  102  103  104  105  0  1.86  66 0  Pre-FACS treatment  2.5  DMSO  Pre-FACS treatment  DMSO  Red-Yellow Ratio  3  Treatment at time of infection  102  103  104  105  LTR - eGFP  Figure 3.1 The frequency of direct LTR-silent infections is modulated by T cell signalling. A: Jurkat cells were treated with DMSO, TNFα, or SAHA at the time of RGH infection. Four days post infection cells were treated with either DMSO or PMA/Ionomycin for 24 hours and analyzed by flow cytometry. Plots shown are representative of triplicate experiments. B: Triplicate data from the experiment in panel A is enumerated as the redyellow ratio of infected cells. Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant, *** p<0.001. C: Jurkat cells infected as in panel A except cells were treated with PMA/Iono at the time of infection. Plots shown are representative of duplicate experiments.  !  57  3.3.2 Direct LTR-silent infections are established early post infection Given that the frequency of LTR-silent RGH infections could be altered (Figure 3.1), we wanted to determine the time frame in which latency could be modulated by TNFα treatment. We infected cells with RGH and sorted them into their constituent populations three days post infection. Cells were left to recover for 24 hours and then treated with DMSO or TNFα for 24 hours. Treated cells were subsequently analyzed by flow cytometry or left to recover in fresh media for another four days prior to further flow cytometry analysis (Figure 3.2A). The latency ratio of unsorted cells treated with DMSO and TNFα showed the typical profile as observed before (Figure 3.1); red cells outnumbered yellow cells for DMSO and the proportions of these populations are reversed after TNFα treatment. DMSO treatment of the sorted negative and red populations showed an increased latency ratio compared to unsorted, indicating that the pre-sorting latency level was fixed (Figures 3.2B and 3.3). This was also the case for DMSO treated sorted yellow cells as they displayed a significantly lower latency level compared to unsorted (Figures 3.2B and 3.3). Activation of the unsorted, negative and red populations resulted in a similar decrease in latency, and TNFα activation of the yellow population showed only a relatively minor drop in latency indicating that the HIV-1 LTR is already fully activated in these cells (Figures 3.2B and 3.3). The sorted negative population showed a low level of positive cells that increased after TNFα treatment, confirming that the negative population contains infected cells silenced at both the LTR and CMV promoters (Figure 3.3). This is consistent with previously activated RGH infected Jurkat cells (Chapter 2, Figure 2.4). Interestingly, when cells were left to recover for four days, negative and red TNFα treated cells returned to latency as reflected by the latency ratio i.e. DMSO and TNFα treated cells were indistinguishable after recovery (Figure 3.2C). Taken together, this suggests that by four days post infection, LTRsilencing is already established and TNFα treatment can no longer permanently alter the proportion of latent cells as it can when administered at the time of infection (Figure 3.1).  !  58  Pre-sorting (3 d.p.i.) 6.88  Post-sorting 92.2%  3.49  Live cell sorting  104  3  102  89.5 103  104  105  LTR - eGFP  LTR - eGFP  Pre-sorting Post-sorting (1 d treatment)  3.52  4 day recovery  16.8 105 12.7  71.7  105  1.22  0.362 105 49.5  3.58 105 11.7  104  104  104  104  103  103  103  103  103  103  2  2  2  2  2  10  95.1 0  105  102  0.109 103  104  0  105  4.34  10  19 0  0.0385 102  103  104  0  105  10  15.1 0  102  0.457 103  104  45.2 105 3.14  8.02 105 39.5  0  10  98.4  105  81.8  0  105  102  0.0585 103  104  1.39  0  105  102  46.9 0  102  0.0101 103  104  0.615 105 50.2  0  105  104  104  104  103  103  103  103  103  103  102  102  102  102  102  0  102  103  104  105  0  15 0  0.315 102  103  104  0  105  14.1 0  102  1.01 103  104  0  98  105  0  0.0214 102  103  104  105  0  0.178 103  104  105  33.7  TNFα  104  0.255  102  6.37 105 3.17  104  87.4  57.9 0  104  0  30.2  DMSO  104  0  eGFP+ mCherry+  Negative 0.4 0.5 1.2 1.5  4 d recovery  104  10  CMV - mCherry  1.26 105 64.1  eGFP+ mCherry+  1 d treatment  1 day treatment 105  DMSO TNFα  0%  eGFPeGFP+ Total inf. Negative mCherry+ mCherry+  71.7 82.0  8.4  20%  eGFP+ mCherry+  DMSO 12.7 TNFα 3.3  0  64.1 39.5  ***  40%  DMSO TNFα  1  60%  3.5  Yellow  2  80%  1.3  100%  4.6  DMSO TNFα  3  % Red-Yellow  **  Red  ***  4 Red-Yellow Ratio  Negative  C  ***  *  DMSO TNFα  B  16.8 eGFPmCherry+ 45.2  eGFP- mCherry+ **  30.2 33.7  102  4 d recovery  FACS  99%  DMSO 11.7 TNFα 3.2  0  0.172  1 d recovery 1 d treatment  FACS  3.6 eGFP6.4 mCherry+  0  91.1%  49.5 50.2  10  CMV - mCherry  CMV - mCherry  105  DMSO TNFα  A  102  43.4 0  102  0.0425 103  104  105  0  62.6 0  102  0.509 103  104  105  LTR - eGFP  Figure 3.2 Direct LTR-silent infections are established early post infection. A: RGH infected Jurkat cells were sorted to greater than 90% purity three days post infection Cells were left to recover for 24 hours prior to treatment with either DMSO or TNFα for a further 24 hours, followed by analysis using flow cytometry. 14  ment over H3  !  12 10 8  H3K9Ac H3K4me3 IgG  *** ***  59  B: Data from the experimental outline in panel B. Cells were analyzed by flow cytometry. Error bars represent standard deviations of duplicate experiments. * p<0.05, ** p<0.01, *** p<0.001. C: Jurkat cells were infected with RGH. Three days post infection, cells were sorted into colored populations (>90% purity) and left to recover for 24 hours. Sorted cells were then treated with either DMSO or TNFα for 24 hours prior to analysis by flow cytometry. Treated cells were then left to recovery for four days prior to further flow cytometry analysis. Data are representative of duplicate experiments. 3.3.3 Direct LTR-silent infections are mediated by cellular activation and NFκB Given that LTR-silent infections can be modulated by TNFα (Figures 3.1 and 3.2), we wanted to see if the cellular activation state, as a whole, was predictive in establishing early latency. To assay for cellular activation in the different cell populations we measured CD69 levels. CD69 is a well characterized early T cell activation marker involved in lymphocyte proliferation and signal transduction (López-Cabrera et al., 1995). When RGH infected cells were stained for CD69, we found that the negative infected fraction expressed similar CD69 levels as uninfected cells (Figure 3.3A). In contrast, red and yellow cells expressed ~2.3 and 4.5 fold more CD69, respectively (Figure 3.3A), indicating that differences in LTR transcription are associated with the state of cell activation. Unlike SAHA, the RGH latent phenotype can be effectively altered by TNFα and PMA/Iono (Figure 3.1), which both stimulate the NFκB pathway (Chen and Goeddel, 2002). To analyze the role of NFκB in our model system more directly, we infected cells with RGH and examined NFκB levels by staining for the DNA binding p50 subunit of NFκB as well as the stimulated form of the transactivating p65 subunit (S529-phospho) four days post infection (Figure 3.3B). NFκB levels were positively correlated with active transcription as negative cells expressed the least of both subunits. Red and yellow cells expressed approximately 1.3 and 1.5 fold more of both subunits, respectively (Figure 3.3B). Importantly, expression of the subunits in red and yellow cells increased together, indicating that escape from latency is associated with higher cellular levels of the active form of NFκB (p65-p50) rather than the inhibitory p50-p50 form. NFκB is the major target of TNFα signaling; however, the stress related JNKMAPK pathway and its downstream factor AP1 are also stimulated (Reviewed in !  60  Chen & Goeddel, 2002). Thus, while TNFα-mediated reduction of RGH latency is likely attributed to NFκB, there could be other pleiotropic effects affecting latency. To address this, we modulated NFκB signaling in a more specific way by using a dominant negative (DN) form of the IκB repressor. However, as NFκB is vital for LTR transcription, signaling must be restored to normal at the analysis time point in order to make any conclusions about latency. To do this, we utilized a doxycycline inducible IκB repressor (S32A/S36A - Kwon et al., 1998), which allows for inhibition of NFκB in a specific and temporal manor. Moreover, as the half life of IκB is very short (Scott et al., 1993), cessation of doxycycline treatment will quickly return NFκB levels to normal. We infected Jurkat cells containing the DN construct with RGH and added doxycycline at defined time points post infection. After doxycycline treatment, cells were cultured in fresh media till analysis by FACS at 4 days post infection. Relative to cells without the DN construct, we observed a significant increase in the latency ratio when cells were treated with doxycycline between one and three days post infection (Figure 3.3C). This suggests that NFκB is necessary and sufficient for modulating RGH latency and, taken together, these data suggest that HIV-1 latency is a function of NFκB activity in cells during a window of four days post infection.  !  61  A  B  NFkB p50  7 ***  6 ***  ***  10000 104  NFkB p65-S529p  ***  *** ***  ***  MFI (x103)  *** ***  5  ***  4  *  ***  3  ns  **  *  2  103 1000  No Dox 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0  1-2 d Dox  1-3 d Dox ***  **  * ns Yellow  Red-Yellow Ratio  C  Red  Cell activation (CD69 MFI)  100000 105  None  DN IκB  Expression construct  Figure 3.3 RGH latency is controlled by NFκB within 4 days post infection. A: RGH infected Jurkat cells (four days post infection) were assayed for cellular activation by CD69 staining and flow cytometry. Uninfected cells were treated with either DMSO or PMA/Ionomycin for 24 hours prior to analysis. Error bars represent standard deviations of triplicate experiments. * p<0.05, *** p<0.001. B: Four days post infection, RGH infected Jurkat cells were stained for NFκB p50 and p65-S529-phospho subunits and analyzed by flow cytometry. Uninfected cells were treated with DMSO or PMA/Ionomycin for 30 min prior to analysis. Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant, * p<0.05, ** p<0.01, *** p<0.001. C: Jurkat cells either with or without a doxycycline inducible dominant negative IκB construct were infected with RGH. Doxycycline (2 μg/mL) was added to the culture for the time periods indicated. Cells were analyzed by FACS at 4 days post infection Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant, * p<0.05, ** p<0.01, *** p<0.001. !  62  105  105  105  105  105  105  104  104  104  104  104  104  3  3  3  3  3  103  102  10  10  10  10  102  102  102  102  102  0  0  0  0  0  0  102  103  104  105  0  102  103  104  105  0  102  103  104  105  0  102  103  104  105  0  0  102  103  104  105  105  105  105  105  105  105  104  104  104  104  104  104  103  103  103  103  103  103  2  2  2  2  2  102  10  10  0  10  0  102  103  104  105  10  0  0  0  102  103  104  105  0  102  14 Fold enrichment over H3  10  0  12  103  104  105  H3K9Ac H3K4me3 IgG  0  0  102  103  104  105  0  102  103  104  105  0  102  103  104  105  TNFα  CMV - mCherry  10  DMSO  In addition to regulation by NFκB, LTR transcription is4 day mediated recovery by a number of 1 day treatment downstream epigenetic mechanisms. Prior to 0.362 transcriptional initiation, LTR 3.52 1.26 64.1 16.8 12.7 71.7 1.22 49.5 3.58 11.7 30.2 nucleosomes (specifically nuc1) become hyperacetylated (Van Lint et al., 1996a). To confirm that epigenetic modifications were also a part of active transcription in the model system, canonical histone 95.1 RGH 0.109 19 0.0385 15.1 we examined 0.457 98.4 0.0585 46.9 activating 0.0101 57.9 LTR 0.178 modifications in 39.5sorted 45.2 RGH infected cells by chromatin immunoprecipitation. 3.14 81.8 4.34 8.02 1.39 0.615 50.2 6.37 3.17 33.7 Consistent with transcriptional activation, H3K9Ac and H3K4me3 were enriched at the LTR (nuc2) in yellow cells relative to red or negative cells (Figure 3.4). Of note,87.4 we could not detect the repressive marks H3K9me3 and H3K27me3 at the 14.1 1.01 0.255 15 0.315 98 0.0214 43.4 0.0425 62.6 0.509 LTR LTR (data not shown), suggesting that activating histone marks occur early as a - eGFP function of LTR expression, and that repressive marks likely occur at later time points. 0  0  102  103  104  105  *** ***  10 8 6 4 2 0  Negative eGFPeGFP- eGFP+ eGFP+ mCherry+ mCherry+ mCherry+  Figure 3.4 Active LTR transcription in RGH infected cells is associated with activating epigenetic marks. RGH infected Jurkat cells were sorted into constituent populations at 3 days p.i. MNase-mediated-ChIP was performed on sorted samples using the following antibodies: H3, H3K9Ac, H3K4me3, and IgG. Results shown are the fold enrichment of the given modification over unmodified H3. Error bars represent standard deviations of triplicate qPCR reactions. Data shown is representative of duplicate independent experiments. *** p<0.001.  !  63  3.3.4 LTR-silent and LTR-active RGH infections are integrated in similar locations Since the site of integration can influence HIV-1 transcription and thereby latency, we wanted to determine if, in addition to NFκB, the location of integration was an important determinant for differentiating between red and yellow RGH infected Jurkat cells. We sorted RGH infected cells into their constituent populations (Negative, eGFP- mCherry+, and eGFP+ mCherry+) and extracted genomic DNA. To identify sites of viral integration, DNA was digested with MseI and ligated to adapters. Nested PCR was used to amplify LTR-host junctions, which were then sequenced by 454 pyrosequencing (Ciuffi and Barr, 2011). Reads were filtered for quality, and mapped to the human genome using the INSIPID pipeline (Berry et al., 2006). We mapped 1195, 2900, and 4271 integrations in the negative, eGFPmCherry+, and eGFP+ mCherry+ populations, respectively. In total, integrations were distributed across the genome (Figure 3.5A), which is consistent with previous reports of HIV-1 integration site selection (Wang et al., 2007; Brady et al., 2009). Furthermore, comparing integrations sites among all chromosomes revealed that each infected cell population exhibited the same distribution of integrations, with no obvious biases towards particular chromosomes (Figure 3.5B). A comparison between the number of integrations within each population shows that integrations were largely overlapping with gene-dense areas for all cell populations and all chromosomes (the representative chromosome 1 is shown, Figure 3.5C). When integrations were plotted against gene density (genes per chromosome), we observed a significant positive correlation (Spearman coefficient ~0.8 – Figure 3.5D), which indicates a preference for HIV1 integrations in gene-rich areas and is consistent with previous reports (Wang et al., 2007; Brady et al., 2009). Additionally, the red and yellow populations contained approximately equal numbers of genic and intergenic integrations (~20%). In contrast, the negative population contained a small but significantly higher proportion of genic integrations (~14% vs. 20% intergenic - Figure 3.5E). However, the orientation within genic regions was not different between cell populations (parallel and anti-parallel orientations – ~40% each, Figure 3.5E).  !  64  B  1  Negative  eGFP- mCherry+  eGFP+ mCherry+  140%  16 19  median  120% 100% 80% 60% 40% 20% 0%  3 5 7 9 11 13 15 17 19 21 X  *  *  *  *  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y  10 1 9 8 7 6 5 4 3 2 1 0  17  % of total integrations  Integration density  A  Chromosome  Chromosome  C  D  Negative eGFP- mCherry+ eGFP+ mCherry+  Integration density  10  chr1 Integration density Gene density  8 6 4 2 0  rho  p value  0.78 0.83 0.85  9.4x10-6 4.9x10-7 2.1x10-6  0 1 2 3 4 Genes per chromosome (x103)  E  80%  38.5  41.8  39.4 ns  46.9  37.4  40.7 ns  14.6*  20.9  19.9  60% 40%  Genic integrations  % of total integrations  100%  20% 0%  Intergenic  Parallel  Anti-parallel  Figure 3.5 Early LTR silencing occurs at all viral integration sites. A: Viral integration sites were mapped by 454 pyrosequencing in RGH infected Jurkat cells sorted for eGFP and mCherry expression. The proportion of total integrations at each chromosome is shown. Data is calculated from 1195 !  65  Negative integrations, 2900 eGFP- mCherry+ integrations, and 4271 eGFP+ mCherry+ integrations. B: The proportion of total integrations at each chromosome is shown for each sample. Data is calculated from 1195 Negative integrations, 2900 eGFPmCherry+ integrations, and 4271 eGFP+ mCherry+ integrations. Error bars represent standard deviations of triplicate experiments * p<0.05. C: Integrations and gene density across 1 Mb windows are plotted for chromosome one. The outer track depicts gene density as annotated in the UCSC hg18 reference genome, while the inner tracks show viral integrations. Chromosome one is highly representative of integrations across the entire genome. C: Integration density is plotted against gene density (genes per chromosome) for each Negative, eGFP- mCherry+, and eGFP+ mCherry+ cells. The straight line is a line of best fit for the plotted points. Samples were tested for significance by Spearman correlation. Coefficients and p values are listed. D: The proportion of integrations parallel and anti-parallel to genes (genic integrations), or intergenic to host cell genes is shown for each sample. Error bars represent one standard deviation between triplicate experiments. ‘ns’ nonsignificant, * p<0.05.  We next compared our samples across multiple genomic features such as gene width/density, distance to transcription start sites (TSS), DNaseI hypersensitivity, CpG density, gene expression levels, etc. (Figures 3.6A and 3.6C). All integration location preferences were consistent with previous studies (Wang et al., 2007; Brady et al., 2009). Importantly, the different populations were similar in most categories, showing little statistically significant differences when compared to the eGFP+ mCherry+ population. Consistent with our earlier analysis (Figure 3.5E), we did observe a significant increase in the number of integrations into genes for the negative population (‘Int. in refGene’ – Figure 3.5A), suggesting that LTR-silencing in the negative population may be regulated differently than red and yellow integrations. Interestingly, when we compared cell populations in the context of previously mapped epigenetic modifications surrounding sites of integrations in Jurkat cells, we observed a statistically significant loss of association with nucleosomes and their post-translational epigenetic modifications for the negative population (Figure 3.6B). Loss of association with nucleosomes in the negative population may be caused by integration into genes with poorly phased nucleosomes; however, further work is needed to elucidate the cause of this phenomenon. Importantly however, we did not  !  66  observe any significant differences between the eGFP+ mCherry+ population and the eGFP- mCherry+ population (Figure 3.6B).  !  67  A  B  Integration density  C  1.2  1.5  1.0  1.0  0.8  Negative  0.6 0  20  40  0.5  y+ y+  60  80  100  % Distance across genes  0  5  10  Distance from TSS (Kb)  Figure 3.6 Early LTR silencing occurs regardless of genomic and epigenetic features at integration sites. A: Integration sites in each sample were compared using the online Insipid heatmap tool for genomic features (Bushman Lab, University of Pennsylvania). Pink and blue colors represent enrichment and depletion of each feature, !  68  respectively, relative to matched random controls of integration sites. Statistical significance is shown relative to the eGFP+ mCherry+ population. B: Epigenetic properties of identified integration sites were compared between samples using the Insipid web tool (Bushman Lab, University of Pennsylvania). Yellow and blue colors represent depletion and enrichment of each feature, respectively, relative to matched random controls of integration sites. Statistical significance is shown relative to the eGFP+ mCherry+ population. Features analyzed were limited to those identified in high-throughput studies of Jurkat and CD4+ T-cells. C: Viral integration density across the entire genome is plotted as a function of the distance across gene bodies (5’ to 3’ – left panel), and the distance from the gene transcriptional start site (TSS – right panel). Raw data is plotted as pale filled circles. Smoothed lines (Loess) have been fitted to each plot.  To further confirm that integration sites are not a major mediator of early latency, we took advantage of the previously described Capsid N74D mutation. This mutation was recently shown to exhibit impaired cyclophillinA-NUP358 binding, thereby bypassing the canonical TNPO3/RANBP2 pathway and entering the nucleus via an alternate nuclear pore complex (Schaller et al., 2011; Ocwieja et al., 2011). As a result, the Capsid N74D mutation causes retargeting of viral integration to regions of lower gene density and activity (increased heterochromatin) (Koh et al., 2013). We created an isogenic RGH variant containing the N74D Capsid mutation and infected Jurkat cells in parallel with wild type RGH. Consistent with the idea that integration site does not affect early latency, we did not observe any differences in the latency ratio between the WT and N74D infected cells (Figure 3.7). Taken together, our integration site data strongly suggests that the early latency observed in the RGH system occurs at all sites of viral integration, and that NFκB is the major determinant of LTR activity in RGH infected cells.  !  69  RGH - WT 105  2.5  2.44  4  103  102 0  1.5  93.3 0  10  0.169 2  10  3  10  4  10  5  RGH - gag N74D  1 0.5 0 WT gag N74D  CMV - mCherry  105  Yellow  Red-Yellow Ratio  ns 2  Red  10  4.07  3.28  1.56  104  103  102 0  94.5 0  102  0.631 103  104  105  LTR - eGFP  Figure 3.7 RGH latency is not affected by heterochromatic integration sites. Jurkat cells were infected with either wild type RGH or a version containing an N74D mutation in gag. Cells were analyzed by flow cytometry four days post infection. Error bars represent standard deviations of triplicate experiments. ‘ns’ non-significant. Representative plots are shown (right).  !  70  3.4 Discussion Using the RGH model system (Chapter 2), we have characterized the previously underappreciated ‘direct silent-infection’ as a contributor to HIV-1 latency. We have shown that silent infections occur irrespective of integration locations (Figures 3.5, 3.6, and 3.7). Instead, the primary means of regulating silent infection appears to be the cellular signalling state, more specifically NFκB levels, during a defined window period early post infection (Figures 3.2, 3.3, and 3.4). Together, this model and these results provide a more thorough understanding of the mechanisms of HIV-1 latency formation. Based on our results, we propose a model in which NFκB is a central and early determinant of latency formation (Figure 3.8). In this model, LTR expression states (‘on’ or ‘off’) and CMV expression levels in infected cells can be explained by a dynamic spectrum of NFκB activity amongst cells in an otherwise homogenous population. In cells with low NFκB, the proviral LTR is silenced by NFκB p50 homodimers bound to the two LTR NFκB sites (Figure 3.8A). p50 homodimers recruit HDAC1 to the LTR, which causes nuc1 deacetylation and low basal transcription (Williams et al., 2006). In cells with high NFκB, the LTR is driven to an active gene expression state by classical p50-p65 NFκB mediated recruitment of the CBP/p300 HAT complex, and other transcriptional coactivators (Lusic et al., 2003; Thierry et al., 2004). Importantly, these deterministic processes are early events (before 4 days post infection), when LTR transcription is likely in the linear Tat-independent phase (Mizutani et al., 2009). This has direct consequences for latency as high Tat-independent transcriptional will drive Tat accumulation to threshold levels, at which point positive feedback between Tat-TAR-pTEF-b will cause fully active HIV-1 expression (Weinberger et al., 2005). Low Tat-independent transcription fails to accumulate enough Tat, causing the LTR to become latent. (Figure 3.8B).  !  71  A  High NFκB  Low NFκB p50 p65  HDAC1  HDAC1  p50 p50  p300  p50 p65  2x NFκB  2x NFκB  HIV-1 LTR  HIV-1 LTR  Deacetylated nuc1  Acetylated nuc1  B productive infection  latency window  Tat level  50 50  Tat threshold  1  50 65  latent infection 50 50  0  <  2  3  >  50 65  4  Days post infection  Figure 3.8 Model of HIV-1 latency as determined by NFκB early post infection. A: Within cells, NFκB is in dynamic equilibrium between low and high states. This equilibrium may be controlled by factors such as stochastic fluctuations (Raj and Van Oudenaarden, 2008), oscillatory behavior (Nelson et al., 2004), or rapid nuclear shuttling (Coiras et al., 2007b). Thus, in an otherwise homogeneous population, many different NFκB levels may exist. In infected cells, variable NFκB levels differentially affect the proviral HIV-1 LTR. Low NFκB results in p50-p50 mediated recruitment of HDAC1, and other repressive epigenetic complexes, to the integrated LTR, thereby suppressing transcription (Williams et al., 2006). High NFκB results in p50-p65 mediated recruitment of the CBP/p300 histone acetyl transferase complex, as well as other transcriptional machinery that can drive high levels of Tat-independent transcription (Lusic et al., 2003; Gerritsen et al., 1997; Zhong et al., 2002). B: HIV-1 latency is established early post infection (1-3 days, ‘latency window’) by NFκB activity (relative p50/p65 ratio) in the host cell during the window period. ‘Repressed’ proviral Tat-independent transcription prevents threshold !  72  levels of Tat needed to activate the tat-TAR-pTEF-b positive feedback loop resulting in latent infection. ‘Activated’ proviral Tat-independent transcription results in enough accumulated Tat to activate positive feedback and achieve active infection (Weinberger and Shenk, 2007).  It is interesting to note that in addition to controlling the HIV-1 LTR, NFκB also plays a role in controlling the promoter and latency of CMV (Hunninghake et al., 1989; Sambucetti et al., 1989; Hummel and Abecassis, 2002). This regulation of CMV by NFκB may help explain some unique aspects of the RGH system. Firstly, we consistently observe a high degree of variability in the CMV-driven mCherry MFI amongst RGH infected cells. This is contrasted with expression of eGFP from the HIV-1 LTR, which consistently exhibits a much narrower expression range. These differences could very well reflect fluctuations in NFκB levels in infected cells, however the LTR’s unique phenotypic bifurcation property limits expression to highly homogeneous binary states, either ‘on’ or ‘off’ (Weinberger and Shenk, 2007; Miller-Jensen et al., 2012). Secondly, we consistently observe that LTR-active cells (yellow) are always those, which exhibit the highest level of CMV-mCherry expression. Thus, expression from both promoters appears to be linked by a common regulatory mechanism that could, very likely, be NFκB. If true, this common link could be responsible for causing the double-silent infected cells (eGFP- mCherry- ‘Negative’ population) observed both in Chapters 2 and 3; double-silent infections may occur when levels of NFκB are sufficiently low enough to silence both promoters. In conclusion, our results suggest that the LTR is immediately silenced in a high proportion of infections. This underappreciated and previously unquantifiable population of initially latent cells has revealed that NFκB signalling is a strong determinant of HIV-1 latency. This may have profound consequences for the establishment and treatment of the latent reservoir in vivo, as latency is not simply the result of epigenetic silencing of once active infections, but rather a very early, and actively decided process.  !  73  3.5 Chapter acknowledgements We thank Andy Johnson and Justin Wong of the UBC Flow Cytometry Facility for live cell sorting and analysis. We also thank Winnie Dong, and Dennison Chan for assistance with pyrosequencing. We thank Nirav Milani for help with the INSIPID pipeline. We also gratefully acknowledge Pauline Johnson for the lentiviral packaging accessory plasmids, Julie Brind’Amour for the ChIP protocol, and Amy Saunders for assistance with CD69 staining. The following reagents were obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH: Jurkat Clone E6-1 from Dr. Arthur Weiss, SAHA (Vorinostat), pHEF-VSVG from Dr. Lung-Ji Chan, and pTY-EFeGFP from Dr. Lung-Ji Chang. This work was supported by Canadian Institute of Health Research (CIHR) grants to I.S. (MOP-77807, HOP-120237), NIH/NIAID grants to V.S. (AI064001, AI089246, and AI90935), and grants to P.R.H. M.S.D is supported by a CIHR fellowship (CGD-96495).  !  74  4 Identification and functional analysis of a second RBF-2 binding site within the HIV-1 promoter 4.1 Introduction In Chapters 2 and 3, we developed a novel model system useful for studying the early determinants of HIV-1 latency. Application of this model in Jurkat T-cells indicated that early LTR-silencing is a common event and is intrinsically controlled by gene expression of HIV-1. Amongst the cellular transcription factors that might control HIV-1 gene expression, and thus latency, is RBF-2. Our lab has had a long standing interest in RBF-2, which binds to a non-canonical EBox element (ACTGCTGA) in the LTR, flanking the distal enhancer (Bell and Sadowski, 1996; Chen et al., 2005; Malcolm et al., 2007, 2008). Functionally, both the viral RBE3 element and the host RBF-2 factor are essential for the transcription of chromosomallyintegrated HIV-1 in response to T-cell activation (Chen et al., 2005; Malcolm et al., 2007, 2008). In addition, integrated LTR reporters with a mutant RBE3 (mRBE3) element show reproducible two-fold increases in basal level expression relative to wild type, suggesting that RBF-2 may bind to the latent, uninduced LTR and contribute to its repression (Chen et al., 2005; Malcolm et al., 2007, 2008). Previous studies have implicated an additional E-box element (CAGCTG), between the TATA box and initiator element (-21 to -16) of the U3 core promoter, in regulation of HIV-1. This element, also known as the 3’ E-box, is capable of binding the CAGCTG-specific, bHLH-ZIP transcription factor, Activating Protein 4 (AP4) (Hu et al., 1990; Mermod et al., 1988; Ou et al., 1994). In the context of HIV-1, overexpression of AP4 in HEK 293T cells appears to cause repression of LTR-driven transcription through multiple mechanisms: 1) AP4 bound to the 3’ E-box can antagonize the binding of TATA binding protein (TBP) in vitro, and 2) AP4 can mediate the recruitment of HDAC1 to the LTR in vivo (Imai and Okamoto, 2006; Ou et al., 1994). Additionally, our group implicated this 3’ E-box element, as well as the RBE3 sequence, in mediating the response to v-Ras activated LTR expression and termed the site Rasresponsive-binding-element-1 [RBE1, (Bell and Sadowski, 1996)]. Indirect evidence suggested that RBE1 and RBE3 may bind the same factor, RBF-2, however this possibility was never tested directly (Bell and Sadowski, 1996). !  75  In this chapter, we sought to examine the role of the RBE elements in controlling HIV-1 latency and transcription. We first tested the elements in the context of the RGH model system and determined that they do indeed mediate HIV-1 latency by activating HIV-1 transcription. Then we further characterized these elements both by biochemical and functional assays. Given the sequence similarity between the viral cis-acting elements RBE1 and RBE3 (core GCTG motif) and the ability of both to positively mediate the response of the LTR to vH-Ras (Bell and Sadowski, 1996), we hypothesized that the transcription factor complex RBF-2 binds to both RBE1 and RBE3. In this study, we analyzed RBE1-bound complexes formed with T-cell nuclear extracts as well as recombinant proteins, and found that the complex formed at RBE1 contained the RBF-2 components USF1, USF2 and TFII-I. This sequence was capable of competing for RBF-2 bound to RBE3 in electrophoretic mobility shift assays (EMSA), and formed a complex of identical mobility as RBF-2 bound to RBE3. We also examined the effect of mutant RBE elements on transcription from the HIV-1 LTR. RBE1 and RBE3 mutants exhibited a modest but reproducible decrease in reporter gene activity while the RBE1/3 double mutant LTR displayed an additive reduction in activity. These observations were extended to virion production experiments and reporter cell infections using full-length HIV-1 molecular clones, which were consistent with findings from the transient LTRreporter assays. Collectively, our results indicate that RBE1 is a novel binding site for the transcription factor complex RBF-2 (USF1/2 and TFII-I). Furthermore, our data suggests that the highly conserved RBE1 and RBE3 elements flanking the HIV-1 enhancer contribute to controlling proper expression of viral gene products in infected cells.  !  76  4.2 Experimental procedures 4.2.1 Protein-DNA interaction assays USF/TFII-I EMSAs were performed as previously described (Malcolm et al., 2008). For EMSA and DNaseI footprinting, USF1, USF2 and TFII-I were produced by expression in Sf21 insect cells from baculovirus vectors (Chen et al., 2005). Recombinant SP1 was obtained commercially (Promega). Jurkat nuclear extracts for EMSA reactions were prepared as previously described (Li et al., 1991). Antibodies used were as follows: AP4 (ab28512, Abcam), TFII-I (Chen et al., 2005), USF1 (H-86, Santa Cruz), and USF2 (N-18, Santa Cruz). Proteins were detected in 10 µg of Jurkat nuclear extract by immunoblotting with the antibodies listed above (1 µg/mL, 4 °C overnight). Double stranded oligos used for EMSA are listed in Table 3.1.  Table 4.1 Double stranded oligonucleotides used in Chapter 4. Name Sequence RBE3 GATCCTTCAAGAACTGCTGACATCGAGCTTTCTC RBE1 ATGCTGCATATAAGCAGCTGCTTTTTGCCTGTACT mRBE3 GATCCTTCAAGAACTGCactCATCGAGCTTTCTC mRBE1 ATGCTGCATATAAGCAGggtgTTTTTGCCTGTACT mAP4 ATGCTGCATATAAGCAGCTcCTTTTTGCCTGTACT The RBE3 and RBE1 core elements are indicated in bold (top two lines). Mutations in competitors are indicated by lower case bold. For labeling purposes, all probe oligos contain an AATT single strand overhang at the 5’ end (not shown).  DNaseI footprinting templates were produced from wild type (LAI) or mutant RBE1 (mRBE1) LTRs by EagI/PstI digestion of plasmids pBS-FPLAI or pBSFPLAImRBE1 respectively, and subsequent gel extraction. Plasmids pBS-FPLAI or pBS-FPLAImRBE1 were created by PCR amplifying a 120 bp fragment of the LTR from either pLAI or pLAI-mRBE1 with oligos oMD018/oMD019 (Table 3.2) and subcloning into the EagI/PstI sites of pBluescriptII KS+ [Fermentas, (Alting-Mees et al., 1992)]. The purified fragments were labelled at the 3’ end (EagI overhang) by end filling with Klenow (New England Biolabs) in the presence of [α-32P]dCTP and [α-32P]dGTP (Perkin Elmer). Unincorporated label was removed using a Sephadex G-50 spin column (GE Healthcare) and the probe was purified using a !  77  DNA spin column (Qiagen). Binding reactions and DNaseI (Promega) digestions were performed as previously described (Malcolm et al., 2008) with the indicated amounts of USF1, USF2, TFII-I, and SP1 protein and approximately 4x104 counts per minute (cpm) of labelled LTR probe. After incubating the proteins with the DNA probe for 30 min at 4 °C, reactions were incubated with 1.75 units of DNaseI for 45 seconds at room temperature prior to the addition of 100 µl DNaseI stop solution (0.2 M NaCl, 20 mM EDTA, 1% SDS, 200 µg/mL proteinase K), and incubation at 55 ˚C for one hour. Following extraction with phenol/chloroform and ethanol precipitation, radioactivity in the pellets was measured in a Beckman LS 3801 scintillation counter. Samples were resuspended in sequencing loading buffer (0.2 mg/mL bromophenol blue and xylene cyanol, 25 mM EDTA, 90% deionized formamide), denatured at 100˚C for 2 minutes, and approximately 2.5x104 cpm were analyzed per lane on an 8% polyacrylamide-urea gel alongside a G+A sequencing ladder produced as previously described (Bencini et al., 1984). Table 4.2 Single stranded oligonucleotides used in Chapter 4. Name oMD018  oMD099  Sequence AAACTGCAGCCAGGGAGGCGTG GC AAACGGCCGGAGAGCTCCCAGG CT CAGCATCTCGAGACCTGG  oMD102  CACCTGACGTCTAAGAAACC  oMD105  ATCCGCTCTAGAACTAGTGGATC  oMD106  GCCGTGCGCGCTTCAGCAAGC  oTM273  CTTCAAGAACTGCactCATCGAGC TTGCTAC GTAGCAAGCTCGATGagtGCAGTT CTTGAAG CAGATGCTGCATATAAGCAGggtg TTTTTGCCTGTACTGGGTCT AGACCCAGTACAGGCAAAAAcacc CTGCTTATATGCAGCATCTG  oMD019  oTM274 oMD100 oMD101  !  Use pLAI forward footprint primer pLAI reverse footprint primer 3’ LTR forward cloning primer 3’ LTR reverse cloning primer 5’ LTR forward cloning primer 5’ LTR reverse cloning primer RBE3 mutagenic primer + RBE3 mutagenic primer RBE1 mutagenic primer + RBE1 mutagenic primer -  78  4.2.2 Cell culture Transformed cell lines: Jurkat T-cells as well as the TZM-bl reporter cells were obtained from the National Institutes of Health (NIH) AIDS Reagent Program (Derdeyn et al., 2000; Platt et al., 1998; Smith et al., 1984; Takeuchi et al., 2008; Wei et al., 2002; Weiss et al., 1984). Human embryonic kidney (HEK) 293T and Sf21 insect cells were obtained from the American Type Culture Collection. Jurkat cells were grown in RPMI 1640 (Sigma) plus 10% fetal bovine serum (Sigma) supplemented with penicillin (100 U/mL) and streptomycin (100 mg/mL) (Gibco), and maintained in a humidified 37 °C, 5% CO2 atmosphere. HEK 293T and TZM-bl cells were grown under standard conditions as previously described (Harari et al., 2009). Sf21 insect cells were grown in Sf-900 II insect media (Gibco) plus 10% fetal bovine serum and maintained at 27 °C. 4.2.3 Transient luciferase expression assays To measure promoter activity, the 3’ LTR from the molecular clone LAI was cloned either with or without the TAR stem loop into the promoter-less reporter vector pGL3-Basic (Promega). To create reporters with TAR, the 3’ LTR was PCR amplified with oligos oMD099 and oMD102 (Table 3.2) from LAI molecular clones and inserted into the KpnI/HindIII sites of pGL3-Basic to create pGL3TAR-WT, pGL3TAR-mRBE3, pGL3TAR-mRBE1, and pGL3TAR-mRBE1/3. To create the identical reporters without TAR, the amplified LTR was instead cloned into the KpnI/SacI sites of pGL3-Basic. The LTR-luciferase reporter constructs used are summarized in Table 3.3. For transient assays, 10 ng of pGL3 reporter plasmid along with 10 ng of either pcDNA3.1+ (Invitrogen) or pcDNA-Tat was transfected into 293T cells using polyethylenimine (Polysciences) as previously described (Durocher et al., 2002). Transfections were performed in 96 well plates seeded with 2x104 cells per well 24 hours prior to transfection. Luciferase activity was measured 24 hours post transfection with the Bright-Glo Luciferase Assay System (Promega) as per the manufacturer’s instructions; 96 well plates were read in a Victor X3 plate luminometer (Perkin Elmer). 4.2.4 Viral strains Plasmid pLAI, containing the complete HIV-1 LAI genome, was provided by Dr. Ben Berkhout (Peden et al., 1991). The RBE1 (CAGCTGC to CAGggtg), and RBE3 (ACTGCTGA to ACTGCact) mutations were produced by PCR mediated !  79  site-directed mutagenesis using the oligonucleotides indicated in Table 3.2, and then cloned into the unique XhoI and AatII restriction sites in the 3’ LTR of pLAI to create pLAI-mRBE1, pLAI-mRBE3, and pLAI-mRBE1/3. Similarly, mutations in the 5’ LTR of LAI were created by PCR mediated site-direct mutagenesis and cloned into the unique XbaI and BssHII sites. All mutations in the 3’ and 5’ LTRs were confirmed by DNA sequencing using the primers oMD099 and oMD105, respectively (Table 3.2). The HIV-1 molecular clones used in this study are summarized in Table 3.3. Table 4.3 Plasmids used in Chapter 4. Name pLAI pLAI-mRBE3 pLAI-mRBE1 pLAI-mRBE1/3 pLAI-mRBE3-5’ pLAI-mRBE1-5’ pLAI-mRBE1/3-5’ pGL3-Basic pGL3TAR-WT pGL3TAR-mRBE3 pGL3TAR-mRBE1 pGL3TAR-mRBE1/3 pGL3-WT pGL3-mRBE3 pGL3-mRBE1 pGL3-mRBE1/3 pBluescriptII KS+ pBS-FPLAI pBS-FPLAImRBE1 pcDNA3.1+ pcDNA-Tat  !  Description HIV-1 LAI molecular clone LAI with mRBE3 site in the 3’ LTR LAI with mRBE1 site in the 3’ LTR LAI with mRBE1 and mRBE3 sites in the 3’ LTR LAI with mRBE3 site in the 5’ LTR and 3’ LTR LAI with mRBE1 site in the 5’ LTR and 3’ LTR LAI with mRBE1 and mRBE3 sites in the 5’ LTR and 3’ LTR Luciferase reporter without promoter (Promega) pGL3-Basic with WT LAI 3’ LTR fragment (KpnI/HindIII) pGL3-Basic with mRBE3 LAI 3’ LTR fragment (KpnI/HindIII) pGL3-Basic with mRBE1 LAI 3’ LTR fragment (KpnI/HindIII) pGL3-Basic with mRBE1/3 LAI 3’ LTR fragment (KpnI/HindIII) pGL3-Basic with WT LAI 3’ LTR fragment (KpnI/SacI) pGL3-Basic with mRBE3 LAI 3’ LTR fragment (KpnI/SacI) pGL3-Basic with mRBE1 LAI 3’ LTR fragment (KpnI/SacI) pGL3-Basic with mRBE1/3 LAI 3’ LTR fragment (KpnI/SacI) Cloning vector (Fermentas) pBluescriptII KS+ with LAI RBE1 Eag1/Pst1 PCR fragment pBluescriptII KS+ with LAI mutant RBE1 Eag1/Pst1 PCR fragment CMV-based mammalian expression vector (Invitrogen) pcDNA3.1+ based HIV-1 LAI Tat expression plasmid  80  4.2.5 Virus production and infection assays Wild type and mutant RBE viral stocks were produced by transfection of molecular clones into HEK 293T cells using polyethylenimine as previously described (Durocher et al., 2002). The molecular clone used and amount transfected are indicated in the details of each experiment. HEK 239T cells were transfected in a 24 well plate seeded 24 hours prior to transfection with 1.5x105 cells per well, except for the experiment in Figure 3.6C where cells were transfected in a 96 well plate seeded with 2x104 cells per well. Viral supernatants were harvested 48 hours post transfection, clarified by filtration (0.45 µm) and stored at -80 °C until use. HIV-1 p24 gag in the culture supernatant was quantified using a commercial ELISA kit as per the manufacturer’s instructions (XpressBio). Proviral expression of the viruses was tested by infecting TZM-bl reporter cells and measuring production of βgalactosidase as previously described (Harari et al., 2009) .  !  81  4.3 Results 4.3.1 RBE elements contribute to proper viral gene expression in the RGH model. To see if the RBE elements function in the context of the RGH latency model, we utilized an isogenic RGH vector bearing mutations in both RBE1 and RBE3 (Figure 4.1). Consistent with their role in mediating proper expression of viral gene products in infected cells, we observed an increase in the ratio of red to yellow cells in the RBE1/RBE3 double mutant relative to wild type (Figures 4.1A and 4.1B). However, when treated with TNFα to activate latent proviruses, we observed no difference, relative to wild type, in the ability of mRBE1/mRBE3 cells to be activated (Figures 4.1A and 4.1B). Taken together, these data are consistent with other viral expression assays (Figures 4.6, 4.7, and 4.8), and suggest that the RBE elements are necessary to mediate proper transcriptional activation of the HIV-1 LTR and, in turn, latency.  B  10  4  103  103  102  102  85 0  105  10  0.186 102  103  104  6.82  0  105  87.8 0  102  0.154 103  104  10.6 105 8.33  4  10  3.5  2.92  105  7.75  4  103  103  102  102  DMSO  TNFα  3 Red-Yellow Ratio  4  0  CMV - mCherry  5.06 105 9.14  DMSO  10  9.72  TNFα  105  RGH – mRBE1/3  Red  RGH - WT  2.5 2 1.5 1 Yellow  A  0.5 0  82 0  0.568 10  2  10  3  LTR - eGFP  10  4  10  5  0  83.5 0  102  0.462 103  104  105  0 RGH - WT  RGH mRBE1/3  Figure 4.1 RBE elements contribute to proper viral gene expression in the RGH model. A: Jurkat cells were infected with either wild type or mRBE1/mRBE3 RGH viruses. Three days post infection, infected cells were treated with either DMSO or TNFα for 24 hours prior to analysis by flow cytometry. Plots shown are representative of duplicate experiments. B: Data from the experiment in panel A is enumerated as the red-yellow ratio of infected cells. Data shown are representative of duplicate experiments. !  82  4.3.2 Proteins in Jurkat T-cell nuclear extracts form complexes of identical mobility at both RBE3 and RBE1 sites Given the similarity between the RBE1 and RBE3 sequences (core GCTG motif), we examined complexes bound to this region of the LTR in Jurkat T-cells (Figure 4.2). We compared protein-DNA complexes formed with Jurkat nuclear extract at both RBE1 and RBE3 by EMSA. A labelled RBE1-containing oligonucleotide probe formed a complex of identical mobility to RBE3-bound RBF-2. Although complexes of identical mobility were formed using both probes, we noted a substantially lower gel shift intensity for RBE1 as compared to RBE3 (Figure 4.3A, compare lanes 1 and 4). Binding of these complexes to both probes could be competed by unlabeled RBE1 or RBE3 oligos (Figure 4.3A, lanes 2-3 and 56), suggesting that the complexes have similar sequence specificity. Additionally, we noted that the RBE1 oligonucleotide produced a slower migrating and uncharacterized complex that was previously designated the TATA binding complex [TBC, (Bell and Sadowski, 1996)]. The RBE3, but not the RBE1, oligonucleotide also formed a complex with another transcription factor YY1, which is consistent with previous data (Malcolm et al., 2008) . These observations indicate that the regions of the LTR containing RBE1 and RBE3 both form complexes with proteins in Jurkat nuclear extract that have similar sequence specificity, in addition to other factors.  !  83  Figure 1 Nuc-0  RBF-2  TFII-I NFAT p50 p65  USF1 USF2  NFAT p50 p65  -150 TGCATCCGGAGTACTTCAAGAACTGCTGACATCGAGCTTGCTACAAGGGACTTTCCGCTGGGGACTTTCCAG -80 RBE3  NF-κB  NF-κB  ACTGCACT mRBE3  SP1  SP1  TFII-D  SP1  Nuc-1  AP4  +1  CATATAAGCAGCTGCTTTTTGCCTGTACTGGGTCTCTCT TATA RBE1 Initiator Box  GGAGGCGTGGCCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATGCTG  -79  SP1  TATAAGCAGGGTG mRBE1  Figure 4.2 Schematic representation of the HIV-1 LTR (LAI) and its highly conserved cis-elements. The mutant RBE3 (mRBE3) and mutant RBE1 (mRBE1) nucleotides are shown in italics within boxes. Core nucleotides shared between RBE1 and RBE3 are underlined. Nuc-0 and Nuc-1 are conserved nucleosomes known to be wellpositioned within the LTR. +1 (arrow) denotes the transcriptional start site.  !  84  Figure 2 B Jurkat Nuclear Extract  RBE1  none  RBE3  Probe RBE1  RBE1  none  Competitor oligo  RBE3  Probe RBE3  A  TATA Binding Complex  200 kDa 116 0  RBF-2 (USF)  53  TFII-I  37 29  Non-specific YY1  7  Non-specific 5  6  α-USF1  Antibody  mRBE3  RBE3  mAP4  mRBE1  RBE1  Free Probe none  Competitor Oligo  Rabbit IgG  4  α-TFII-I  3  α-AP4  C  2  α-USF2  1  ss TFII-I TATA Binding Complex RBF-2 (USF) Non-specific  Non-specific 1  2  3  4  5  6  7  8  9  10 11 12  Figure 4.3 Proteins in Jurkat nuclear extract produce identical complexes with RBE1- and RBE3-containing oligonucleotides. A: EMSA was performed with Jurkat nuclear extracts and labelled RBE3 (lanes 1-3) or RBE1 probes (lanes 4-6). Unlabeled RBE3 (lanes 2 and 5) or RBE1 (lanes 3 and 6) competitor oligos were added at 100-fold molar excess. Migration of previously defined USF, TFII-I, YY1, TATA-binding complexes and nonsequence specific bands are indicated. B: Jurkat nuclear extracts (10 µg) were separated on a 12% SDS-PAGE gel and transferred to nitrocellulose membrane. Proteins were detected by immunoblotting with α-AP4, α-TFII-I, α-USF1, or α-USF2 antibodies. !  85  C: EMSA was performed with Jurkat nuclear extracts and a labelled RBE1 probe. RBE1 (lane 3), mutant RBE1 (lane 4), mutant AP4 (lane 5), RBE3 (lane 6), or mutant RBE3 (lane 7) unlabelled competitor oligos were added at 50-fold molar excess. 1 µg of antibodies to USF1 (lane 8), USF2 (lane 9), AP4 (lane 10), or TFII-I (lane 11) were added to determine complex constituents. 1 µg of rabbit IgG was added (lane 12) as an antibody isotype control. Non-sequence specific complexes are indicated. Migration of RBF-2, TATA binding complex, and a TFII-I supershift (ss) species are indicated. Data shown are representative of three independent experiments.  4.3.3 RBE1-Jurkat nuclear extract complexes contain RBF-2 and are distinct from AP4 The RBE1 cis-element spans a consensus E-box previously shown to bind AP4 (Imai and Okamoto, 2006; Ou et al., 1994). Given the fact that at RBE1 we observed a complex with identical properties to RBF-2, we probed the identity of proteins bound to oligonucleotides spanning this region using specific antibodies. We confirmed that AP4, TFII-I, USF1, and USF2 were present in Jurkat T-cell nuclear extracts using immunoblotting (Figure 4.3B). We next performed EMSA with a radiolabelled RBE1-containing oligo and Jurkat nuclear extracts. In the absence of any competitor oligos or antibodies, we observed complexes corresponding to both RBF-2 as well as the previously described TBC complex (Figure 4.3C, lane 2). The former complex could be competed away by excess unlabelled RBE1 (Figure 4.3C, lane 3) or RBE3 oligonucleotides (Figure 4.3C, lane 6) but not by oligonucleotides bearing a mutation within RBE1 that, in conjunction with a mutation in RBE3, was previously shown to prevent stimulation of the LTR by v-Ha-Ras (mRBE1 – Figure 4.3C, lane 4) (Bell and Sadowski, 1996), a mutation shown to inhibit binding of AP4 (mAP4 – Figure 4.3C, lane 5) (Imai and Okamoto, 2006; Ou et al., 1994), or a mutation of the RBE3 element (mRBE3 – Figure 4.3C, lane 7) (Malcolm et al., 2008). These results indicate that the complex formed with Jurkat nuclear extracts at RBE1 specifically recognizes the 3’ E-box element flanking the TATA box and likely contains RBF-2. To further examine the interaction of RBF-2 at RBE1, we added antibodies against RBF-2 constituents as well as AP4 to the EMSA reactions. Antibodies against USF1 (Figure 4.3C, lane 8) and USF2 (Figure 4.3C, lane 9) both prevented formation of the RBF-2 complex, whereas an antibody against TFII-I formed a supershifted species (Figure 4.3C, lane 11). Surprisingly, antibodies against AP4 did not disrupt any of the complexes formed with the !  86  RBE1 oligonucleotide, including the RBF-2 complex; moreover, no supershifted species were observed with the AP4 antibody (Figure 4.3C, lane 10). Taken together, these results suggest that at endogenous levels of protein in Jurkat Tcell nuclear extracts, RBE1 is preferentially bound by RBF-2 rather than by AP4, and that RBE1 is a bona fide RBF-2 binding site. 4.3.4 Recombinant USF oligonucleotide  and  TFII-I  bind  an  RBE1  containing  To confirm that USF and TFII-I are capable of binding to the RBE1 element 3’ of the TATA box, we performed EMSA with recombinant USF1 and USF2 produced in baculovirus infected Sf21 insect cells. When co-expressed, USF1 and USF2 produced three complexes bound to an RBE3-containing oligonucleotide representing USF2 homodimers, USF1/USF2 heterodimers, and USF1 homodimers (Figure 4.4A, lane 1). The identity of these complexes was verified by the inclusion of α-FLAG and α-USF2 antibodies, which caused a supershift of FLAG-tagged USF1 (Figure 4.4A, lane 5) and native USF2 (Figure 4.4A, lane 6). Binding of each of the complexes could be competed with unlabeled wild type RBE3 oligonucleotide (Figure 4.4A, lane 2), but not an RBE3 mutant oligonucleotide (Figure 4.4A, lane 3). Importantly, complexes with identical mobility were observed in EMSA reactions using an RBE1-containing probe (Figure 4.4A, lanes 7- 12). Binding of the USF complexes could be competed by excess unlabeled wild type RBE3 or RBE1 oligonucleotides (Figure 4.4A, lanes 8 and 10), but not by mutant RBE3 oligonucleotides (Figure 4.4A, lane 9). Furthermore, complexes formed with recombinant USF on the RBE1 oligo were supershifted by the inclusion of specific antibodies (Figure 4.4A, lanes 11 and 12). These findings suggest that the RBE1 E-box element immediately downstream of the TATA box is bound by recombinant USF1/2 heterodimers, consistent with the results obtained with Jurkat nuclear extracts (Figure 4.3).  !  87  Figure 3  USF2 USF USFFLAG  1 2  mRBE3 RBE1 α-FLAG α-USF2  mRBE3 RBE1 α-FLAG α-USF2 none RBE3  none RBE3  Competitor  3 4 5 6 7 8 9 10 11 12  Probe RBE3  Probe RBE1  6.25 pmol USF  TFII-I  Competitor  α-FLAG α-USF2 α-HA RBE1 mRBE1 none  100 pmol TFII-I  TFII-I USF2 USF USFFLAG TFII-I 1 2  3 4 5 6 7 8 9 10 11 12 13  Figure 4.4 Recombinant USF1/USF2 and TFII-I (RBF-2) bind RBE1, immediately 3’ of the HIV-1 TATA box. A: EMSA reactions were performed with insect cell extracts expressing USF1FLAG and USF2, and labelled RBE3 (lanes 1-6) or RBE1 (lanes 7-12) probes. Unlabeled competitor RBE3 (lanes 2 and 8), mutant RBE3 (lanes 3 and 9), or RBE1 (lanes 4 and 10) oligos were added at 100-fold molar excess. 2 µg αFLAG antibody was added in lanes 5 and 11, and 2 µg of α-USF2 antibody was added to reactions in lanes 6 and 12. Migration of USF1 and USF2 are indicated. Data shown are representative of two independent experiments. B: EMSA reactions were performed with approximately 6.25 pmol recombinant USF protein (lanes 1-12), and labelled RBE1 probe. TFII-I was added to the binding reactions at 3 pmol (lane 2), 6 pmol (lane 3), 12 pmol (lane 4), 25 pmol (lane 5), 50 pmol (lane 6) or 100 pmol (lanes 7-13). 2 µg of α-FLAG, α-USF2, and α-HA antibodies were added to the reactions in lanes 8, 9, and 10, respectively. Unlabeled competitor RBE1 (lane 11) or mutant RBE1 (lane 12) oligos were added at 100-fold molar excess. Migration of USF and TFII-I complexes are indicated. Data shown are representative of two independent experiments.  !  88  4.3.5 TFII-I enhances the USF- RBE1 interaction in vitro To examine the involvement of TFII-I in binding of USF to RBE1, we determined whether recombinant TFII-I was capable of stimulating binding of USF to RBE1 in an EMSA reaction, similar to the effect previously shown for binding of USF to RBE3 (Malcolm et al., 2008). We found that addition of TFII-I at an approximately 10-fold molar excess caused a 2-3-fold enhancement of USF binding (Figure 4.4B, lanes 2-7). This effect is somewhat smaller than the 10-fold enhancement by TFII-I for binding of USF to the non-canonical RBE3 site (Malcolm et al., 2008). This difference is likely attributed to the fact that RBE1 contains an E-Box motif (CAGCTG), to which USF heterodimers are capable of binding on their own (Corre and Galibert, 2005). Interestingly, recombinant TFII-I itself formed several complexes with the RBE1 probe in EMSA reactions (Figure 4.4B, lane 13), which might reflect binding of TFII-I monomers to multiple sites, or as a multimer to a single site. We have not fully characterized the DNA binding specificity for TFII-I in this region of the LTR, but we noted that a mutation within the E-box motif (CAGCTGC to CAGggtg) prevented competition for binding to USF1 and USF2, but not to the TFII-I specific complex (lane 12). This suggests that TFII-I must bind to sequences flanking the E-box. Consistent with this, it was previously demonstrated that USF and TFII-I bind cooperatively at both Ebox and Inr elements within the Adenovirus major late (AdML) promoter (Roy et al., 1991). These results, taken together with previous observations (Bell and Sadowski, 1996; Chen et al., 2005; Malcolm et al., 2007, 2008), confirm that USF and TFII-I are bound together at both RBE1 and RBE3, which flank the core promoter and enhancer regions of the HIV-1 LTR. 4.3.6 Interaction of RBF-2 at RBE1 can be observed by DNaseI footprinting In order to confirm that USF binds directly overlapping the E-box element spanning RBE1, we performed DNaseI footprinting analysis of recombinant RBF-2 constituents on the core HIV-1 LTR promoter region. A template of approximately 120 nucleotides, spanning the 5’ most SP1 binding site (-82) to the TAR stem loop (+42) was used for footprinting with recombinant USF1/2 and TFII-I expressed in baculovirus infected Sf21 insect cells. In the absence of TFIII, USF1/2 protected a region of approximately 20 nucleotides centered over the RBE1 E-box (Figure 4.5A, lanes 3 and 4). Consistent with the EMSA results shown above, addition of TFII-I to footprinting reactions containing a minimal amount of USF1/2 caused enhanced protection of this same region of the LTR (Figure 4.5A, lanes 7 and 8). However, TFII-I alone did not produce specific !  89  protection, despite the above observation that it forms multiple complexes with the RBE1 probe in EMSA (Figure 4.4B). Similar observations were noted previously for interaction of TFII-I with the AdML initiator element (Roy et al., 1991), and with the upstream HIV-1 RBE3 element (Malcolm et al., 2008). Binding of USF/TFII-I to RBE1 produced a footprint larger than that of the E-box consensus sequence itself (Figure 4.5A, lanes 3-8). USF, alone or in combination with TFII-I, protected a region of approximately 20-25 nucleotides including the TATA box and the RBE1 motif, as well as approximately five nucleotides on both sides. This result is similar to footprinting assays for USF/TFII-I binding to the RBE3 element and USF to the upstream E-box (-166 to -161), where binding protects approximately 20 nucleotides centered over both elements (Malcolm et al., 2008). This may reflect natural sites of non-specific contact between USF and its cognate binding site. Indeed, it has been previously described that USF protects approximately 20 nucleotides on the AdML (Roy et al., 1991) and Human Apolipoprotein C-III promoters (Pastier et al., 2002). Mutation of the RBE1 element completely abolished protection by USF in DNaseI footprinting assays (Figure 4.5B, lanes 4-9), either alone or in combination with TFII-I. Taken together, DNaseI footprinting support the contention that RBE1 serves as a binding site for RBF-2.  !  90  USF  TFII-I  TFII-I  SP1  SP1  TATAA mRBE1  1  2  USF USF  TFII-I  TFII-I  gtggGACGAATAT  SP1  5’ SP1  Template  USF  GTCGACGAATAT  RBE1  TATAA  5’  Template  B WT MG G+A  A  mRBE1 MG G+A  Figure 4  +1 +1  3’  1  2  3  4  5  6  7  8  9  10  3’  3  4  5  6  7  8  9  10  Figure 4.5 USF binds to the TATA-proximal RBE1 element and binding is stimulated by TFII-I. A: DNase 1 footprinting reactions were performed with a wild type LAI LTR fragment, labelled at the 3’ end of the top strand. Reactions contained either template alone (lane 2), or template plus 5 (lane 3) or 10 µl (lane 4) of USF1/2 insect cell lysate, 5 (lane 5) or 10 µl (lane 6) of TFII-I insect cell lysate, or 10 µl USF1/2 lysate plus 5 (lane 8) or 10 µl (lane 9) of TFII-I lysate. Lane 9 contained 1 µg SP1 protein while lane 10 contained 10 µl of an insect cell lysate expressing yeast TEC1 protein. The positions of the TATA box and RBE1 were determined by a Maxim and Gilbert G+A chemical cleavage ladder run alongside (lane 1). Data shown are representative of two independent experiments. B: DNase 1 footprinting reactions were performed with a mutant RBE1 LTR fragment (CAGggtg). Reactions contain the same amounts and combinations of proteins as in panel A.  !  91  4.3.7 RBE elements mediate transcriptional activation in transient assays Despite the extensive sequence diversity of HIV-1, the RBE1 and RBE3 sequences are highly conserved amongst clinical isolates, suggesting that these two elements are important for HIV-1 replication (Estable et al., 1996, 1998, 1999; Jeeninga et al., 2000; Malcolm et al., 2008). Since USF and TFII-I have been shown to have both activating and repressive effects depending on context [promoter, cell type, and cellular activation state - Reviewed in (Corre and Galibert, 2005; Roy, 2007; Yang et al., 2002)], and the viral RBE1 and RBE3 elements are both bound by RBF-2, we sought to evaluate the role of these elements in the regulation of HIV-1 expression. In order to test the role of the RBE elements in mediating both basal transcription as well as Tat-activated transcription, we created LTR-luciferase reporter constructs bearing mutant RBE elements, both with and without the TAR stem loop. Given the stimulatory effect of Tat-transactivation when TAR is present (Berkhout et al., 1989), we transiently transfected HEK 293T cells with the LTR-luciferase reporter constructs both in the presence or absence of the viral transactivator Tat. Figure 4.6 shows that, regardless of TAR and Tat, the RBE3 and RBE1/RBE3 mutant LTRs resulted in a two-fold decrease in reporter activity compared to wild type. Additionally RBE1 mutant reporters exhibited a more modest 25% reduction in activity relative to wild type. This pattern of activity amongst the LTR reporters was consistent between all combinations of TAR and Tat tested (Figure 4.6). When TAR and Tat were both present, luciferase activity was strongly induced (approximately 15-fold increase compared to the expression level measured in the absence of Tat). Furthermore, the same relative expression pattern was apparent among wild type and mutant promoters suggesting that the RBE elements do not alter the ability of the LTR to respond to Tat-mediated activation but rather regulate basal level activity (Figure 4.6). Taken together, these data confirm that intact RBE elements are required for efficient LTR transcription and therefore, may play a role in regulating HIV-1 expression.  !  92  Figure 5 U3  - 455 RBE3  R  +78 Luciferase  RBE1  TAR  Tat  +1  Luciferase 2000  140 120  1500  100 1000  80 60  500  40  Relative Activity (% of WT)  Relative Activity (% of WT)  160  AAAAA  Empty Vector WT mRBE3 mRBE1 mRBE1/3  20 0  0  TAR  -  -  +  +  Tat  -  +  -  +  Figure 4.6 RBE elements mediate transcriptional activation in reporter assays. Schematic: LTR-Luciferase constructs contained LTR sequence from nucleotides -455 to +78 fused to luciferase. Constructs lacking TAR contained nucleotides -455 to +34. Data: The transcriptional activity of LTR-luciferase constructs was measured in transiently transfected 293T cells. 10 ng of either a TAR-lacking or TARcontaining wild type, mutant RBE3-, mutant RBE1-, or mutant RBE1/3luciferase reporter construct was transfected into 293T cells either with or without 10 ng of HIV-1 Tat expression vector. Luciferase activity was measured in the cell lysate 24 hours post transfection. Cells were transfected in triplicate, and data were averaged and made relative to the activity of the wild type reporter without TAR or Tat. Error bars represent one standard deviation in the triplicate values. Data shown are representative of three independent experiments. !  93  4.3.8 RBE elements are required for efficient HIV-1 virion production In order to assess the role of the RBE elements in HIV-1 expression and production, we constructed full-length LAI molecular clones with mutations of the RBE elements in only the 3’ LTR or in both the 3’ and 5’ LTRs. As expression of the molecular clones is driven by the 5’ LTR, mutations present in the 3’ LTR will only affect LTR activity after reverse transcription and infection of target cells (Figure 4.7A). We generated wild type and mutant viral stocks by transfection of 293T cells with a low amount of DNA (50 ng) and quantified virion production in the supernatants by p24 ELISA (Figure 4.7B). As expected, we observed no difference in p24 levels between the 3’ LTR mutants and wild type (Figure 4.7B). In contrast to the 3’ LTR mutants, virion production was affected by the 5’ LTR mutants. The 5’ LTR RBE3 and RBE1 single mutants showed a 25 and 60% decrease in levels of p24, respectively, which was further reduced in the RBE1/3 double mutant (approximate five-fold reduction, Figure 4.7B). These data are consistent with the results of the LTR-luciferase assays and support the idea that the RBE elements may play a role in mediating proper HIV-1 expression. We next wanted to examine the effects of the mutant RBE elements in the context of integrated proviruses by comparing the viruses’ expression in a single cycle assay using TZM-bl reporter cells. TZM-bl cells are a HeLa-derived cell line that express the HIV-1 entry receptors CD4, CXCR4 and CCR5, and contain an integrated HIV-1 promoter that drives expression of β-galactosidase in response to infection-mediated Tat expression. We transfected 293T cells with molecular clones bearing mutations in the 3’ LTR only and used virus stocks to infect TZM-bl reporter cells (Figure 4.7C). The RBE single mutants exhibited a modest but reproducible reduction in expression from the integrated provirus as compared to wild type. In contrast, the RBE1/3 double mutant exhibited a sizable, five-fold decrease in proviral expression relative to wild type (Figure 4.7C). These data are consistent with the transient promoter assay and viral expression from plasmids, suggesting that HIV-1 expression from integrated proviruses is similarly affected by the RBE mutations. However, when we compared the effects of the mutations with two different amounts of transfected DNA, we found that that the effect of the RBE1 and RBE3 mutations is more pronounced at lower levels of viral input (compare 50 ng to 100 ng inputs Figure 4.7C).  !  94  Figure 6 A  DNA  U3  R U5  gag  pol  env  5’ LTR  U3  R U5  3’ LTR Transcription  mRNA  R U5  gag  pol  env  U3  R AAAAA  U3  R U5  Reverse transcription Integration  Provirus  U3  R U5  gag  pol  5’ LTR  3’ LTR  Relative p24 (% of WT)  100 80 60 40 20 0  C m3'LTR  m5'LTR  Relative Infectivity (% of WT)  B 120  env  120  50 ng  100 ng  100 80 60 40 20 0  Figure 4.7 RBE elements are required for efficient HIV-1 expression in cell culture. A: Schematic representation of the replication of a HIV-1 molecular clone bearing targeted mutations in its 3’ LTR. In cells transfected with an HIV-1 molecular clone bearing mutations in its 3’ LTR, virion production is controlled by the 5’ LTR (wild type) and these virions will contain the targeted mutations in the 3’ LTR of their RNA genome. Upon infection, the viral genome is reverse transcribed such that the U3 region of the 3’ LTR is copied to the 5’ LTR. In the integrated provirus, targeted mutations are present in both the 5’ and 3’ LTRs. Subsequent virion production is then subject to the effects of any mutations present. B: Virion production by 293T cells transfected with 50 ng of LAI molecular clones containing mutant RBE elements in either their 3’ LTR only or both their 5’ and 3’ LTRs. Levels of p24 protein in the viral supernatants were measured by ELISA 48 hours post transfection and made relative to wild type levels. C: Proviral expression was measured by single-cycle TZM-bl assay. Wild type and mutant RBE LAI viruses were produced by triplicate transfection of 293T !  95  cells with 50 or 100 ng of molecular clones bearing mutant RBE elements in their 3’ LTRs only. 48 hours post transfection, viral supernatant was used to infect TZM-bl cells and β-galactosidase activity in TZM-bl lysate was measured 48 hours post infection. Mean values are plotted relative to the expression of the wild type virus at each input level and error bars represent one standard deviation in TZM-bl values.  To further examine the effects of viral input on the phenotype of the RBE mutants, we titrated wild type and 3’ LTR mutant viruses on TZM-bl cells. Consistent with the previous experiments, the single RBE3 and RBE1 mutants exhibited decreased expression, which was further decreased in the double mutant. We again observed a more pronounced decrease in infectivity for the mutants at lower inputs (Figure 4.8). Furthermore, the difference in relative infectivity of the RBE1 and the RBE3 mutants was affected by the amount of virus. Mutant RBE1 had significantly higher infectivity than mutant RBE3 at high input, but similar infectivity when less virus was used. These results are consistent with those in Figure 4.7C and, taken together, suggest that the effects of the RBE mutations may be affected by concentration (DNA or virus), although the consequences of this are currently unknown. Collectively, our results indicate a role for the RBE3 and RBE1 elements in the proper regulation of HIV-1 gene expression through activation of LTR-driven transcription.  !  96  Figure 7  Relative Infectivity (% of WT)  120  WT  mRBE3  mRBE1  mRBE1/3  100 80 60 40 20 0  Virus Dilution Factor  Figure 4.8 Infectivity defects of mutant RBE viruses are affected by input. The integrated proviral expression of wild type and mutant RBE viruses was measured over a range of viral inputs. Viral supernatants were produced as in Figure 3.6 and diluted by the given factors prior to triplicate infection of TZM-bl cells. β-galactosidase activity was measured 48 hours post infection; mean values are plotted relative to the expression of the wild type virus at each dilution and error bars represent one standard deviation in TZM-bl values.  !  97  4.4 Discussion HIV-1 exploits host cell transcription factors to regulate its initial expression from the proviral LTR. Different mechanisms ensure transcription in different cell types and cellular activation states (Pereira et al., 2000; Sadowski et al., 2008). In activated T lymphocytes, a combination of factors responsive to T-cell signalling pathways (NF-κB, NFAT, AP1 and GABP/Ets) permits robust transcription of the provirus (Brooks et al., 2003; Robichaud et al., 2002). Conversely, reversion to a resting memory CD4+ T-cell state results in repression of the provirus through reorganization of the LTR into repressive chromatin (Han et al., 2007; Mok and Lever, 2007), thereby leading to postintegration molecular latency (Reviewed in Coiras et al., 2009; Colin and Van Lint, 2009; Lassen et al., 2004; Richman et al., 2009; Trono et al., 2010). Among the elements that mediate transcriptional regulation of the HIV-1 LTR are two elements that flank the core viral enhancer, RBE1 and RBE3. In this study, we demonstrated that the RBE1 element is a novel binding site for the transcription factor complex RBF-2. In both EMSA and DNaseI footprinting experiments, RBF-2 constituents USF1, USF2 and TFII-I bound the RBE1 E-box (Figures 4.3, 4.4, and 4.5). Of note, we were unable to detect AP4 in complexes bound to RBE1 in EMSA experiments with Jurkat T-cell nuclear extracts, whereas USF1, USF2 and TFII-I did form complexes with this element as detected by specific antibodies (Figure 4.3C). We were able to detect AP4 protein expression in Jurkat nuclear extracts by immunoblotting (Figure 4.3B), and it is possible that binding of RBF-2 is mutually exclusive of AP4. Additionally, the effect of AP4 on transcription from the LTR may be modified by the signalling state of the cell, dictated by various post translational modifications, and/or be subject to regulation by other transcription factors recruited to the promoter. As such, whether or not RBF-2 is a binding partner or competitor of AP4, it may be that at endogenous levels of Jurkat T-cell nuclear protein, the effect of AP4 is masked by the action of RBF-2. Although further work will be needed to elucidate this relationship, we clearly observe that single and double mutations of RBE1 and RBE3 decrease transcription from the LTR, virion production, and proviral expression (Figures 4.6 and 4.7). Importantly, these effects are the opposite of what would be expected for RBE1 if a repressor protein like AP4 is the sole factor binding this site. Interestingly, binding of multiple host transcription factors to the same cis-element within the HIV-1 LTR has been observed before, most notably the duplicated NF-κB/NFAT  !  98  sites (Bates et al., 2008; Cron et al., 2000; Kinoshita et al., 1997; Koken et al., 1992; Michael et al., 1994). Analyses of transcription, virion production and proviral expression suggest that, together, the RBE1 and RBE3 elements mediate the proper transcriptional activation of HIV-1. Given that the two elements bind the same factor in vitro, it is not surprising that these elements would have similar effects on LTR-driven transcription. However, previous work by our group using stably integrated LTR reporters in clonal cell lines has shown that RBE3 mutants exhibit a modest but reproducible increase in basal transcription, thus suggesting a dual role for RBE3 in which it can also act as a mediator of transcriptional repression (Chen et al., 2005; Malcolm et al., 2007, 2008). While these differences could be a result of the lack of proper chromatin environment in transient assays, we note that the results of our transient experiments were consistent with assays of proviral expression in TZM-bl cells. It is also interesting to note that the Jurkat cell lines used in our previous studies were subject to long-term passaging prior to the assessment of promoter activity. This time frame would allow the integrated retroviral reporters to be epigenetically silenced (Ellis, 2005), and this silencing could be differentially affected by the RBE3 mutation, thus implicating RBE3 in mediating long-term transcriptional repression. Reduced silencing of the mutant RBE3 reporter could be caused by less efficient RBE3-RBF-2 mediated recruitment of silencing factors to the LTR or due to the reduced initial LTR activity prior to the establishment of silencing. In both scenarios, the overall level of silencing in the mutant LTR would be reduced relative to wild type, and the RBE3 element, along with RBF-2, would appear to mediate transcriptional repression. Further work is needed to elucidate the role of RBF-2 in mediating proviral silencing and latency as well as to reconcile the duality of function RBF2 seems to possess. Regardless of any involvement in epigenetic silencing, our data indicate that both the RBE1 and RBE3 elements are involved in coordinating efficient LTR activity; thus, future experiments are needed to elucidate the precise mechanism(s) of RBE-mediated transcriptional control. Specifically, chromatin immunoprecipitation (ChIP) experiments coupled with siRNA knockdown of RBF-2 factors should allow for the elucidation of RBF-2 function at RBE1 and RBE3 in integrated, chromatin bound LTRs, both in the basal and activated states. However, the close proximity of the RBE elements (~100 bp apart), and the presence of an additional USF binding site ~40 bp upstream of RBE3 (Coiras et al., 2009; Colin and Van Lint, 2009; Lassen et al., 2004; Richman et al., 2009; !  99  Trono et al., 2010), makes it technically challenging to use ChIP and siRNA analysis to delineate between RBF-2 at both RBE elements. It is interesting to note however, that in RBE1 and RBE3, the HIV-1 LTR contains two highly conserved sites that bind the same factor (RBF-2). In vitro evidence suggests that the leucine zipper domain of USF is capable of forming higher-order homotetramer complexes at intracellular protein concentrations (Ferre-D’Amare et al., 1994). While it is unclear if this interaction occurs in vivo, or how the presence of TFII-I would impact potential interactions, higher order USF structures may potentiate LTR looping interactions that could contribute to additional levels of transcriptional regulation. Fully characterizing the function of RBF-2 at the RBE elements, as well as other HIV-1 transcriptional regulators bound to the LTR, is of considerable clinical interest since modulating their functions may produce novel therapeutic interventions that target not only replicating viruses but also latent HIV-1 proviruses. Novel approaches may allow for targeted and controlled reactivation of latent reservoirs by inhibiting transcriptional repressors and/or stimulating transcriptional activators. Conversely, it may be possible to permanently repress these reservoirs by stimulating repressor function in a cell type specific manner. Thus, the development of novel therapeutics to robustly modulate HIV-1 transcription would provide welcome new weapons for use in the eradication of HIV-1.  !  100  4.5 Chapter acknowledgements We thank Ben Berkhout for providing the molecular clone pLAI and Asavari Jatiani for performing p24 ELISA. We also thank Lubbertus Mulder and Andrea Kunz for help with viral production and replication assays as well as insight and thoughtful discussions. We thank Ting-Cheng Su for assistance with EMSA experiments. We also gratefully acknowledge Jacob Hodgson, Sheetal Raithatha, and Adam Chruscicki for helpful discussions and consult. The following reagents were obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH: Jurkat Clone E6-1 from Dr. Arthur Weiss, and TZM-bl from Dr. John C. Kappes, Dr. Xiaoyun Wu and Tranzyme Inc. This work was supported by a Canadian Institute of Health Research (CIHR) grant to I.S. (MOP-77807) and a NIH/NIAID grant to V.S. (AI064001, AI089246, and AI90935). V.S. is a Sinsheimer Scholar (Alexandrine and Alexander L. Sinsheimer Fund). M.S.D is supported by a CIHR fellowship (CGD-96495). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  !  101  5 Conclusion 5.1 Chapter summaries The overall goals of this thesis were to construct and characterize a novel, double-labeled model of HIV-1 latency, and to utilize it to study both early LTRsilent infection, as well as the contribution of RBF-2/RBE to basal transcription and, in turn, HIV-1 latency. The research aims and results of this thesis are summarized in Figure 5.1. 5.1.1 Chapter 2: Detection of early silent HIV-1 infection using a doublelabeled red-green reporter virus In chapter two, we constructed and characterized a novel, double-labeled model of HIV-1 latency (RGH). While traditional singly labeled models have been very useful to date, they are inherently limited by their design. Without a way to track infection independently of the HIV-1 LTR, these models require selection steps and extensive culturing to identify infected cells and establish latency. Thus, this design precludes the study of latency establishment and potentially biases the model by failing to discriminate between initial (direct) and secondary (progressive) LTR silencing. To mitigate these pitfalls, we constructed a HIV-1 vector bearing an LTR-eGFP reporter, as well as an LTR-independent CMV-mCherry reporter. This design allows for identification of infected cells regardless of the transcriptional status of the LTR. In this way, direct silent infection can be identified and characterized distinctly from progressive LTR silencing. We showed that in Jurkat T cells, direct silent infections are a very common event, and that these infections are a bona fide property of HIV-1, rather than a system specific event. We also showed that the majority of LTR-silent infections are transcriptionally competent, as they can be reactivated by a variety of T cell signaling agonists and epigenetic modifying drugs. Finally, we showed that the RGH model effectively identifies and recapitulates differences in latency between natural LTR subtype variants that have been previously described (Jeeninga et al., 2000; van der Sluis et al., 2011). Taken together, these data indicate that the RGH model is an effective system to study the early events of HIV-1 latency in an unbiased and systematic way.  !  102  5.1.2 Chapter 3: HIV-1 latency is established early post infection and is regulated by NFκB In chapter three, we used the RGH model system to dissect the mechanisms of early HIV-1 latency and direct LTR-silent infection. Having determined that these infection events are common, we identified NFκB as a primary determinant of early HIV-1 silencing. The occurrence of direct silent infections could be reduced by treatment with TNFα within four days post infection. Furthermore, direct silent infections correlated with both cellular activation status (CD69 expression) and NFκB levels. Finally, when we modulated NFκB during the infection process (expression of a dominant negative IκB) we found that NFκB was necessary and sufficient to regulate RGH latency. We also examined the impact of integration site on latency in the RGH model. We found that LTR-silent infections occur at all sites of integration and that, consequently, site selection does not regulate this process. Collectively, these results indicate that HIV-1 latency, at least in the RGH system, is established early post infection and is regulated by NFκB. Thus, direct silent infections may have a profound impact on the formation of the latent reservoir in vivo. 5.1.3 Chapter 4: Identification and functional analysis of a second RBF-2 binding site within the HIV-1 promoter In chapter four, we studied the effects of RBF-2 and the RBE elements on RGH latency and basal LTR transcription. We first examined RGH latency in a mutant RBE1/RBE3 background and found that latency was potentiated relative to wild type. We next characterized the biochemical properties of RBF-2 binding to both the RBE1 and RBE3 elements within the HIV-1 LTR. We determined that RBF-2 indeed binds both elements and that, in contrast to RBE3, RBE1 is a higher affinity EBox element. We also characterized the functional aspects of the RBE elements in terms of basal transcription. By transient transfection and stable transduction, we found that the RBE elements are necessary for efficient LTR transcription. This is consistent with their effect in the RGH model and, taken together, suggests that early HIV-1 latency is primarily determined by NFκB, and then modulated by accessory transcription factors like RBF-2.  !  103  Crosstalk? NFκB  RBF-2 RBE1/RBE3  Other transcription factors  AP1  Modulation by other factors?  Modulation by integration site Direct silent infection  in vitro  Productive infection  ?  Progressive epigenetic silencing  in vivo  Red-Green-HIV-1 (RGH)  Latently infected CD4+ T cells (quiescent memory cells) Latency modulating drugs  Reservoir maintenance Half-life: ~ 44 months Homeostatic proliferation  Antigen presentation  Latent cell and proviral reactivation  Reversion to G0 memory T cell state  ?  Studied in this thesis  Outstanding questions  Infected cell death (viral cytopathic effects or immune CTL clearance) Literature knowledge  Figure 5.1 Summary of research aims and results in this thesis. Ideas directly studied are shown in black, outstanding questions are shown in blue, and concepts in the literature are shown in grey.  !  104  5.2 Control of HIV-1 transcription and latency 5.2.1 NFκB 5.2.1.1 Transcription and reactivation NFκB is one of the most well characterized and important factors involved in HIV-1 expression. Indeed, NFκB is a potent and indispensible driver of LTR transcription (Karn and Stoltzfus, 2012; Colin and Van Lint, 2009; Pazin et al., 1996; Asin et al., 2001; Atwood et al., 1994; Quivy et al., 2002; Wu et al., 1995; Brooks et al., 2003; Burnett et al., 2010, 2009). NFκB has also been shown to be an important signal for reactivating latent proviruses (McNamara et al., 2012; Lassen et al., 2012; Williams et al., 2004; Coiras et al., 2007a). Consequently, attention has been given to a number of potential latency modifying drugs, like prostratin, jatrophane diterpene (SJ23B), 12-deoxyphorbol 13-phenylacetate (dPP), and 5-hydroxynaphthalene-1,4-dione (HN), that all act as agonists of the PKC pathway and ultimately induce NFκB signaling (Choudhary, 2011). Interestingly however, a recent study by Wolschendorf et al., 2012 has identified the presence of a currently unknown kinase whose activity is necessary for efficient HIV-1 reactivation despite high NFκB activity. In the study, the authors identified and characterized a small molecular JNK kinase inhibitor (AS601245) that effectively restricted HIV-1 reactivation in cells treated with potent PKC agonists like PMA, prostratin, or TNFα (Wolschendorf et al., 2012). This data suggests that there is yet another layer of complexity to the regulation of the HIV-1 LTR, which we are just starting to uncover. More importantly, these results suggest that, in addition to targeting canonical HIV-1 regulatory pathways, potential latency modulating therapeutics must also target this upstream kinase in order to be efficacious. 5.2.1.2 Silent infection To our knowledge, only one study has identified the contribution of direct silent infection to HIV-1 latency, and implicated NFκB as an early determinant (Duverger et al., 2009). In that study, which used a singly labeled vector to infect Jurkat T cells, the authors did not find compelling evidence that latency was the product of progressive epigenetic silencing. Instead, they identified direct silent infection as the main contributor to latency (Duverger et al., 2009. Moreover, they concluded that silent infection was correlated with NFκB signaling at the !  105  time of infection, and that transcriptional interference from integration into actively transcribed genes was the main mode of HIV-1 silencing maintenance (Duverger et al., 2009). Importantly, the data of Duverger et al., 2009 are consistent with what we have observed in this thesis with the RGH model. We have expanded on these ideas by showing that direct silent infection is established within four days post infection and that NFκB alone is sufficient to modulate silent infection (Chapters 3.3.2 and 3.3.3). Importantly, Duverger et al., 2009 used PKC agonists like PMA, prostratin, or TNFα to perturb silent infection, all of which are non-specific and have pleiotropic effects. 5.2.2 AP1 Supplementary to NFκB, AP1 (c-fos/c-jun) is another factor implicated in controlling HIV-1 transcription. Like NFκB, AP1 is responsive to both the PKC and Ras/MAPK pathways downstream of the T cell receptor. Classically, AP1 is known to be a transcriptional activator both at cellular genes, as well as the HIV1 LTR (Karin et al., 1997; Colin and Van Lint, 2009). Recently however, an AP1 binding site within the core promoter has been implicated in directly controlling latency formation (Duverger et al., 2012). It has been well established that, in contrast to the prototypical HIV-1 subtype B, the recombinant subtype AE is highly active and significantly less prone to the establishment of latency, while subtypes A and C are more prone to latency formation (Van der Sluis et al., 2011; Jeeninga et al., 2000). We have also observed the same phenomenon in this thesis using the RGH vector (Chapter 2.3.5). By examining the different subtype LTRs, Duverger et al., 2012 determined that a unique four nucleotide AP1 binding site just upstream of the distal NFκB site in the core promoter is responsible for the latentcy profiles of the different subtype viruses. Subtype B contains a partial four nucleotide AP1 sequence while subtypes A and C contain a higher affinity seven nucleotide AP1 sequence at this position. In contrast, the entire AP1 site is deleted in the AE LTR (Duverger et al., 2012). While these results are not ammenible to a simple causal relationship, they suggest that transcription factor binding at the LTR is vital to controlling HIV-1 latency. More importantly, they suggest that in addition to NFκB, AP1 is another key regulator of HIV-1 latency (Duverger et al., 2012). !  106  Interestingly, this regulation by AP1 appears to be distinct from its traditional role as a transcriptional activator, as the results observed by Duverger et al., 2012 are the opposite of what might be expected if AP1 were merely activating transcription. Indeed, the recent study by Wolschendorf et al., 2012, which identified an upstream kinase necessary for HIV-1 reactivation, may be linked to the idea of latency control by AP1. The inhibitor identified by Wolschendorf et al., 2012 (AS601245) is a JNK inhibitor, and AP1 is a major substrate of the JNK MAPK stress pathway. Thus, there appears to be a complex interplay between all of these factors in regulating HIV-1. Indeed, we observed that, in the RGH model, modulating only NFκB was sufficient to significantly alter the formation of latency within four days post infection (Chapter 3.3.3). However, we also observed that RBF-2/RBE was sufficient to alter latency in the RGH model (Chapter 4.3.1). Interestingly, unpubished data generated in our lab suggests that the putative AP1 site identified in Duverger et al., 2012 also serves as a binding site for TFII-I (as part of RBF-2 bound to RBE3) and YY1. Taken together, these various lines of evidence suggest that a large amount of interplay between factors is present in this region of the HIV-1 LTR. Not surprisingly, this region is highly conserved amongst HIV-1 clinical isolate sequences (Estable et al., 1996), further underscoring the importance of this region in controlling HIV-1 transcription. Indeed, it is not uncommon for HIV-1 LTR cis-elements to bind multiple transcription factors depending on cellular state and specific context. Well characterized examples of this include the overlapping NFκB/NFAT sites (Bates et al., 2008), as well as the AP4/RBF2/RBE1 site (Imai and Okamoto, 2006; Dahabieh et al., 2011). Ultimately, further research is needed to conclusively delineate the contributions of NFκB, AP1, and potentially JNK and RBF-2, to the establishment and maintenance of HIV-1 latency. Moreover, it seems imperative to conclusively determine whether direct silent infection is a specific function of a few key factors (e.g. NFκB or AP1), or whether it is a function of generalized transcriptional output driven primarily by these key factors and then modulated by accessory factors (e.g. RBF-2).  !  107  5.3 HIV-1 latency fluctuation Through the development and application of the RGH vector, we have demonstrated in this thesis that direct silent infections are a common event (Chapters 2 and 3). It is puzzling that supposedly identical virions may infect a supposedly homogenous cell population and produce the phenotypic variability we observe. Why are some proviruses active, yet others latent? In studying this phenomenon, we have implicated NFκB as a driving force behind this variability, and have put forth a model in which NFκB fluctuations drive the differences in latency we observe (Figure 3.8). The question remains how and why NFκB levels fluctuate between otherwise homogenous cells. Within any given population, stochastic fluctuations in transcription factor levels and gene expression occur due to biochemical and thermodynamic processes; moreover, these fluctuations can be substantial enough to drive phenotypic asymmetry in clonal cells (Raj and van Oudenaarden, 2008; Raser and O’Shea, 2005). The NFκB and SP1 sites of the LTR have been implicated in controlling stochastic HIV-1 gene expression (Burnett et al., 2009), presumably through fluctuations in the availability of NFκB and SP1 themselves. Furthermore, stochastic fluctuations in Tat levels have been described to account for phenotypic bifurcation of the HIV-1 promoter (Weinberger et al., 2005; Singh and Weinberger, 2009). However, in the both cases it is unclear what drives fluctuations in NFκB/SP1 and Tat, respectively. Alternatively, evidence also exists to suggest that signalling fluctuations may be less stochastic and more coordinated. For example, because NFκB drives the expression of its own inhibitor (Scott et al., 1993; Sun et al., 1993), NFκB signalling has been described to exhibit oscillatory behaviour in response to TNFα stimulation, with a periodicity of ~100 minutes that persists for ~ 20 hours post treatment (Nelson et al., 2004). Furthermore, while steady-state conditions place NFκB p65 and its inhibitor IκB in the cytoplasm, both proteins undergo rapid shuttling between the cytoplasm and nucleus, even in unstimulated cells (Coiras et al., 2007b). Shuttling NFκB was shown to provide low-level basal HIV1 transcription in infected resting CD4+ T cells (Coiras et al., 2007b). Conversely, shuttling of IκB has also been shown to bind and remove active NFκB p65 from target sites in the genome in order to dampen leaky NFκB signalling (ArenzanaSeisdedos et al., 1997). This process has also been shown to inhibit HIV-1 transcription in resting CD4+ T cells and was proposed to be a method of continual latency control (Coiras et al., 2007b). Combined with stochastic !  108  fluctuations, both forms of oscillatory NFκB behaviour may contribute to a wide spectrum of functional NFκB levels in Jurkat cells, thereby contributing to HIV-1 latency.  5.4 Multiple routes to HIV-1 latency In the years following the discovery of the latent reservoir, science has made great advancements in our understanding of the reservoir’s molecular underpinnings. Canonically, HIV-1 latency has been thought of as the progressive silencing of otherwise active infections (Siliciano and Greene, 2011; Colin and Van Lint, 2009). Through the development and application of the double-labeled RGH vector, work in this thesis suggests that direct silent infection may also be an important and common contributor to HIV-1 latency (Chapters 2 and 3). Regardless of whether it forms as a result of direct or progressive mechanisms, HIV-1 latency is, fundamentally, a product of proviral transcriptional silencing. In turn, silencing is a product of a multitude of transcriptional regulatory mechanisms (Karn and Stoltzfus, 2012). These mechanisms (described in Chapters 1.4 and 1.5) include the concerted actions of the ~30 transcription factors that bind the HIV-1 LTR, as well as the associated epigenetic and chromatin modifications that are targeted/recruited through these transcription factors. Work in this thesis and by many other groups all support a model in which latency is controlled, most directly, by the viral transactivator Tat (Chapter 1.5.6 and Figure 3.8). The unique promoter architecture of HIV-1 dictates that if a threshold level of Tat is reached, positive feedback yields near maximal transcription output. However, if, for any combination of reasons, Tat levels do not reach the threshold, transcriptional output will be zero or near zero (latency) (Weinberger et al., 2005, 2008; Singh and Weinberger, 2009; Singh et al., 2010; Weinberger and Shenk, 2007). Given the positive feedback architecture and the multitude of factors and mechanisms that control HIV-1 transcription, it seems highly probable that there may be multiple ways in which latency could form. Any combination of factors that limit transcription, and thus Tat synthesis, could result in HIV-1 latency, regardless of whether it is a direct silent infection or progressive proviral !  109  silencing. In the latter case, numerous model systems and publications have identified the transcriptional regulatory mechanisms that may be progressively applied in a combinatorial fashion to drive latency (Siliciano and Greene, 2011; Colin and Van Lint, 2009; Karn and Stoltzfus, 2012). In the former case, which until now has been underappreciated, our data using the RGH vector suggests that direct LTR-silent infections appear to be mediated predominantly by the action of NFκB i.e. low NFκB results in silent infection (Chapter 3.3.3). Given the inefficiencies of early Tat-independent transcription (Karn and Stoltzfus, 2012), it is logical that a potent and essential factor like NFκB would appear to play a predominant role in regulating direct silent infection. However, the Tat threshold model of HIV-1 regulation also implies that it is probable for additional factors to be involved in modulating direct silent infections. Indeed, we also observed a contribution from RBF-2/RBE to the occurrence of RGH LTR-silent infections (Chapter 4). Despite understanding many of the individual components involved in HIV-1 latency, our understanding of how these mechanisms fit together cumulatively is very poor, if not non-existent. That is, we simply do not understand the undoubtedly complex and multifaceted ways in which HIV-1 transcriptional regulatory mechanisms act in concert to yield the final latent phenotype. For example, a recent study by Miller-Jensen et al., 2012 established a direct and measurable link between proviral chromatin accessibility and the NFκB threshold needed to activate a synthetic HIV-1 LTR reporter. By using a sensitive doxycycline-inducible p65 construct in conjunction with clones bearing the HIV-1 reporter in different chromatin environments, the authors demonstrated that, in their model, HIV-1 transcriptional activation is a function of both chromatin accessibility (heterochromatic fraction) and p65 levels; as accessibility drops, more p65 is needed to activate HIV-1 transcription, and vice versa (Miller-Jensen et al., 2012). Thus, it is not difficult to imagine a scenario in which neither the effects of integration site/chromatin accessibility, nor NFκB are sufficient to silence HIV-1 on their own; only when poor chromatin accessibility is coupled to low NFκB is latency established. In this model, the coupled effects of only two factors were studied, and yet the relationship between the factors is complex. In vivo, where HIV-1 transcription may be simultaneously controlled by dozens of mechanisms, the path from individual factors to the final latent phenotype is almost certainly far more intricate.  !  110  5.5 Future directions In this thesis, we have constructed and characterized a double-labeled RGH vector that may be applied to a broad range of important research questions concerning HIV-1 transcriptional regulation. Thus, it should be of substantial utility to other groups studying various aspects of HIV-1 latency, including those who use model systems to screen for novel therapeutics to purge the latent reservoir in vivo. When considering potential drugs for HIV-1 reactivation, the paramount concern is to avoid global T cell activation and the resultant cytokine storm that would induce massive immune toxicity in patients (Ward et al., 2006). In HIV-1 drug screens to date, candidate compounds are tested for global T cell activation in a low-throughput manner. Importantly however, the RGH vector offers a significant advantage over other HIV-1 latency models used in drug screens. Since the CMV promoter is highly responsive to T cell activation pathways (Hummel and Abecassis, 2002; Froberg, 2004; Reeves and Sinclair, 2008; Sambucetti et al., 1989; Hunninghake et al., 1989), the CMV-mCherry reporter could be used as a surrogate marker of T cell activation. Thus, it should be possible to test for T cell activation in a high-throughput manner, concurrently with the screen for HIV-1 reactivation compounds. HIV-1 specific hits should yield a high LTR-eGFP to CMV-mCherry ratio, while general T cell activating compounds would yield a ratio closer to one (both promoters activated). Importantly, a screen of this type may also reveal novel biological pathways, as HIV-1 specific hits would indicate activation of signalling pathways distinct from the T cell activation pathway, with which HIV-1 activation is canonically associated. Our studies in this thesis also present a number of other interesting avenues for future research: 1. Primary cell HIV-1 latency models are thought to better recapitulate quiescent memory CD4+ T cells and, thus, it is highly desirable to characterize the RGH vector in this context. Furthermore, the work of the Planelles group have allowed for the polarization of resting CD4+ T cells into multiple memory lineages (Bosque and Planelles, 2009), all of which could be characterized with the RGH vector. Finally, the RGH model could be studied in the context of other cell types permissive to HIV-1, such as dendritic cells and/or macrophages; these cells could be of either transformed or primary origin. !  111  2. In this thesis we have characterized the RGH vector in terms of early LTRsilencing. By extended culturing to allow epigenetic mechanisms to silence actively infected cells, it should be possible to use the RGH vector to study HIV1 latency formation from a previously active state. Consequently, this would also allow for analysis of both latency maintenance and re-activation mechanisms. 3. While we have focused on the contributions of NFκB to RGH latency, it would also be interesting to specifically test other cellular factors. In light of the recent Duverger et al., 2012 paper implicating AP1 as a primary determinant of HIV-1 latency, this factor would be amongst the most interesting to test. As part of a larger project, it would also be extremely helpful to systematically characterize the contribution of a large panel of factors to HIV-1 latency (Table 1.1), both individually and in combination. 4. It would also be useful to increase the temporal resolution of the RGH vector. This could be accomplished by utilizing an unstable GFP variant, d2EGFP, which has a half-life of only 3.6 hours (Corish and Tyler-Smith, 1999; Pearson et al., 2008). Relative to the normal RGH vector, this should allow for tracking of eGFP levels that more accurately represent the current HIV-1 LTR transcriptional state, rather than accumulated eGFP protein. 5. The recent study by Miller-Jensen et al., 2012 suggests that HIV-1 latency may be a complex function of multiple factors acting together in unpredictable ways. One of the more variable factors in the problem is the semi-random integration of proviruses across the genome; consequently, it would be useful to study the action of the ‘other’ factors in the context of identical integration sites. To accomplish this it would be necessary to retarget HIV-1 integration. A study by Ferris et al., 2010 demonstrated that retargeting is indeed possible through the usage of LEDGF/p75 fusion proteins. By expressing a LEDGF/p75-HP1 chromodomain fusion in cells at the time of infection, the authors were able to retarget HIV-1 to heterochromatic regions. Although fairly crude for our purposes, this study provides a proof of principle for HIV-1 retargeting. By transiently expressing LEDGF/p75-TAL effector or zinc finger fusions during RGH infection, it should be possible to retarget viral integration in a highly predictable and specific manner. This would allow for more uniform integration amongst cells and could remove a significant confounding variable when studying the impact of non-integration site factors on HIV-1 latency.  !  112  References Abraham, R. T., and Weiss, A. (2004). Jurkat T cells and development of the T-cell receptor signalling paradigm. Nature reviews. Immunology 4, 301–308. Ahluwalia, J. K., Khan, S. Z., Soni, K., Rawat, P., Gupta, A., Hariharan, M., Scaria, V., Lalwani, M., Pillai, B., Mitra, D., et al. (2008). Human cellular microRNA hsa-miR-29a interferes with viral nef protein expression and HIV-1 replication. Retrovirology 5, 117. Alting-Mees, M. A., Sorge, J. A., and Short, J. M. 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